GPS World

Category: Research & Development

  • Innovation: Software GNSS Receiver

    Innovation: Software GNSS Receiver

    An Answer for Precise Positioning Research

    By Thomas Pany, Nico Falk, Bernhard Riedl, Tobias Hartmann, Günter Stangl, and Carsten Stöber

    Innovation Insights with Richard Langley
    Innovation Insights with Richard Langley

    WHAT IS THE IDEAL GNSS RECEIVER? Well, that depends on what you mean by “ideal.” If we take it to mean the simplest, conceptually, yet the most capable and adaptable receiver, then we would just connect an analog-to-digital converter (ADC) to an antenna and pass the converter’s output to a digital signal processor whose software would transform the received signal into the desired result with the utmost speed and precision. There are certain technological limitations that currently preclude fully developing such a device but we are getting very close to the ideal and practical implementations already exist.

    Such a GNSS receiver is an example of a software-defined radio — a radio-communications architecture in which as much of the operation of a receiver (or a transmitter) as feasible is performed by software in an embedded system or on a personal computer (PC).

    Now, we can’t simply connect an ADC to an antenna since the strength of GNSS signals falls well below the sensitivity threshold of real ADCs and those that can directly digitize microwave radio frequencies are rather power hungry. Therefore, the front end of a real software GNSS receiver includes a low-noise preamplifier, filters, and one or more downconverters to produce an analog intermediate-frequency signal that passes to a high-speed ADC. The digitized signal is provided at the output of the front end in a convenient format, which, for processing signals on a PC, is typically USB 2.0 with its maximum signaling rate of 480 megabits per second. All baseband signal processing is then carried out in the programmable microprocessor.

    Software GNSS receivers offer a number of advantages over their hardware cousins. Foremost is their flexibility, which permits easy and rapid changes to accommodate new radio frequency bands, signal modulation types and bandwidths, and baseband algorithms. This flexibility is beneficial not only for multi-GNSS operation but also for prototyping algorithms for conventional hardware designs. Another advantage is their use in embedded systems such as smartphones and wireless personal digital assistants. Software GNSS receivers are also a boon for teaching, where a student can tweak a particular operating parameter and immediately see the effect. And given their remarkable flexibility, software GNSS receivers can be adapted to a variety of special scientific and engineering research applications such as reflectometry and signal analysis.

    In this month’s “Innovation,” we look into the development and capabilities of one modern software GNSS receiver in an effort to answer the question “What is the ideal GNSS receiver for precise positioning research?”

    “Innovation” is a regular feature that discusses advances in GPS technology andits applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick.

    Personal-computer-based software receivers have found broad use as R&D tools for testing new signal processing algorithms, for analyzing received GNSS signals, and for integrating various sensors with GNSS. Software receivers also provide a consistent framework for GNSS signal samples, correlator values, pseudoranges, positions, assistance data, and sensor (inertial) data, and often act as integration platforms for prototype navigation systems. The distinctive feature of PC-based software receivers is their ability to work in post-processing mode in addition to real-time operation and the support of high-performance central processing units (CPUs).

    So far, software receivers are typically not used as operational receivers — neither in the mass market, nor in the professional sector, nor as a reference station where a PC would already be available. The last point can be explained by the fact that most software receivers can only process a limited number of frequency bands (sometimes just L1) and are often limited to small bandwidth signals (such as that of the L1 C/A-code signal or the L2 civil signal (L2C)). Improvements of the PC-based software receiver SX-NSR achieved at the end of 2010 and in early 2011 try to overcome these limitations. They include the first real-time implementation of P-code processing on L2, a unique method for processing the ultra-wide Galileo AltBOC signals on E5, and a method to synchronize two NavPort-4 frontends (each supporting four frequency bands of 15 MHz bandwidth) via a hardware link.

    The SX-NSR, which has been developed in cooperation with the Universität der Bundeswehr München in Munich, Germany, runs under the Windows operating system (XP or 7) and supports processing of GNSS signals plus sensor data (such as that from an inertial measurement unit, or IMU) in real time and in post-processing mode. It supports all the civil GPS, GLONASS, Galileo, and Compass signals. User-defined signals can be included by providing the pseudorandom noise (PRN) codes and the associated tracking parameters.

    The computational real-time performance can be characterized by two rules-of-thumb for acquisition and tracking. Acquisition is based on a flexible coherent and noncoherent integration and may be accelerated by a graphics card based on the Compute Unified Device Architecture (CUDA) — a parallel-computing architecture developed by Nvidia for graphics processing but also useful for accelerating non-graphics applications. Depending on the graphics card type, a few million or many millions of equivalent correlators are available to detect even the weakest signals quickly. Stable tracking is done with multiple correlators. An x86 CPU core supports around 20 channels (for a laptop) to 30 channels (for a PC) at an average CPU load below 50–60 percent. With that CPU load, the software has enough reserve (in terms of the size of the sample buffer) to cope with latencies introduced by the non-real-time Windows operating system. In post-processing, a virtually unlimited number of channels or correlators is available.

    The SX-NSR software typically connects to the NavPort-4 front end via a single USB 2.0 connector. One front end supports four RF paths with 15-MHz bandwidth in the L-band. One band is sampled at 40.96 MHz with 12-bit precision. Small batches of samples are transferred with 12 bits at regular intervals to the PC for increased spectral analysis possibilities but the continuous transfer is usually done with just 2 bits. Decimation by a factor of two (yielding a sample rate of 20.48 MHz) and/or bit reduction are options to limit the data transfer bandwidth on the USB bus. The NavPort also includes configurable notch and finite-impulse-response (FIR) filters working with 12-bit precision and 40.96-MHz data rate. The SX-NSR further supports standard output formats (such as Receiver Independent Exchange (RINEX) format or that of the Radio Technical Commission for Maritime Services (RTCM)), has a graphical user interface, and a freely user-accessible application programming interface (API) in the C programming language.

    A reference station was established with the SX-NSR for the International GNSS Service (IGS) Multi-GNSS Experiment (M-GEX) starting on February 1, 2012, at the Observatory Graz in Austria (marker name GRAB). The data is routinely processed by the European Reference Frame analysis center at Observatory Lustbuehel, Graz, Austria, with Bernese Software 5.0, and shows results with a quality that is virtually no different than that of commercial hardware receivers.

    All-in-view tracking of the four GNSS constellations on all frequencies (see TABLE 1) has been achieved with an off-the-shelf $1,000 PC, two synchronized NavPorts, and the SX-NSR software. In particular, the front end receives Compass B1, B2, and B3 signals and currently the software is tracking two of the geostationary Earth orbit (GEO) satellites, a few of the inclined geosynchronous orbit (IGSO) satellites, and the medium Earth orbit (MEO) satellites at Graz on B1 and B2. There are plans to implement tracking of the B3 signal for the M1 satellite and that of satellite-based augmentation system (SBAS) satellites on L5.

    Table 1. Frequency bands supported by the dual NavPort-4 receiver.
    Table 1. Frequency bands supported by the dual NavPort-4 receiver.

    Typical received carrier-to-noise-density-ratio (C/N0) values recorded at station GRAB are shown in FIGURE 1. Freely accessible GRAB data in RINEX format can be downloaded from several data archive sites (see Further Reading online).

    The SX-NSR software receiver provides a GNSS development and research framework with the API opening it up for user-implemented algorithms. The user may implement only small portions of new code (such as a special acquisition technique) and for the rest of the receiver rely on the well-known behavior of the SX-NSR’s framework. A number of applications are possible using the flexibility of a software receiver; some of them are described in this article.

    Figure 1. C/N0 values for five typical satellites, one each for GPS, GLONASS, Galileo, Compass, and the European Geostationary Navigation Overlay Service (EGNOS) SBAS as recorded at Observatory Graz.
    Figure 1. C/N0 values for five typical satellites, one each for GPS, GLONASS, Galileo, Compass, and the European Geostationary Navigation Overlay Service (EGNOS) SBAS as recorded at Observatory Graz.

    The Front End

    The front-end frequency plan was adjusted to have a clean spectrum free of internal interference. This is of utmost importance as software receivers are often used to detect and mitigate interference especially for the Galileo E5 and E6 frequency bands due to overlapping radio navigation services.

    An inter-front-end link enables synchronization of two NavPort-4 devices. It generates a synchronous reference clock for a proper phase relationship. Moreover, a trigger is used to adjust the digital data stream of both devices. One possible application of the inter-front-end link technology is to easily double the number of available GNSS frequencies. Another possible application is the building of a dual-antenna solution. For this purpose, each NavPort-4 device handles the same GNSS frequencies, but from different antennas. Whereas within each NavPort, a quad analog-to-digital converter (ADC) synchronously samples the four received GNSS signals, the synchronicity between two NavPorts is more complex.

    For the inter-front-end link, both devices have to use the same 10-MHz clock reference for a synchronous setup. This is reached by using the reference clock output of the master device as reference clock input of the slave device as depicted in FIGURE 2. It is also possible to connect both NavPort-4 devices to a single external clock reference.

    Figure 2.
    Figure 2.

    Each device generates its own 40.96-MHz sample rate from this reference. The phase difference of the 40.96-MHz sample rate is measured in the master and slave with a phase detector. The first input of the detector is the local 40.96-MHz clock. The second input is the 40.96-MHz clock from the other NavPort-4 with a different phase alignment due to ambiguities in its generation and cable delay. The phase detector measures the phase difference between both clocks. The low-pass-filtered output of this measurement is digitized with an ADC. If this measurement is within a phase range of ±7 degrees at 40.96 MHz, which corresponds to ±14 centimeters, the coarse synchronization is finished. If the value is not within this range, the synchronization algorithm repeats.

    After starting the data processing for both devices simultaneously with an implemented digital trigger, the phase difference between master and slave clock could be measured continuously for later fine-tuning in the SX-NSR to achieve an accuracy of much below 1 degree at 40.96 MHz, which corresponds to ±2 centimeters.

    The synchronization algorithm is verified by connecting two L1-capable NavPorts to the same antenna. The phase and code delay can then be determined from receiver single-differences of GPS L1 C/A-code-derived phase and code measurements. Actually, this delay estimation is part of an attitude solution (see below) and an example is shown in FIGURE 3. The code delay here is around 50 centimeters and includes the RF filter delay difference of around 40 centimeters (which can be calibrated and is stable over power cycles) in addition to the synchronization delay (here around 10 centimeters). The phase delay is naturally determined modulo one cycle and shows warm-up effects of 1.4 centimeters during the first few minutes of operation.

    Figure 3. Inter-front-end hardware delay variation on a GPS L1 signal.
    Figure 3. Inter-front-end hardware delay variation on a GPS L1 signal.

    GNSS Reference Station

    Although the GPS modernization process is ongoing and more and more L2C-capable satellites are in orbit, tracking of the encrypted P-code signal on L2 is still a key element for any receiver to be considered as a reference station or geodetic receiver. Dual-frequency observations need to be available for the full GPS constellation. A possible option to retrieve full wavelength carrier-phase observations and code ranges on L2 is cross-correlation tracking of the encrypted P-code signal. The receiver computes the cross-correlation function between the raw L1 and L2 samples over a long coherent interval as shown in FIGURE 4. The encrypted P-code stream, identical on L1 and L2, is represented by c(tµ).

    Figure 4. Cross-correlation block diagram.
    Figure 4. Cross-correlation block diagram.

    A receiver internal complex carrier is generated (see frequency compensation in Figure 4), whose frequency equals the Doppler shift frequency plus the intermediate-frequency difference between L1 and L2. This frequency is generally different for each satellite. The L1 signal is delayed to compute the cross-correlation function for several code-phase taps.

    The cross-correlation function is computed using the predicted Doppler difference based on the Doppler frequency estimated from L1 with complex-valued baseband samples. A number of batches are collected and a post-correlation fast Fourier transform is applied. The parameter values shown in TABLE 2 result in a total coherent integration time of 6.4 seconds.

    Table 2. SX-NSR cross-correlation parameter values.
    Table 2. SX-NSR cross-correlation parameter values.

    The result is the cross-correlation function as a function of code phase and Doppler. Using interpolation techniques, the position of the peak is determined, which then gives the delay and Doppler shift of the L2 signal with respect to the L1 signal. The complex argument of the peak value gives the L2-L1 carrier-phase differences. Those differences are filtered and are then added to the L1 parameters to give the L2P code estimates.

    We use two first-order Kalman filters (one for the code, one for the phase) to smooth the cross-correlation estimates. The code filter is updated with the estimated delay and the Doppler; the phase filter is updated with the estimated Doppler and phase. Cycle slips are detected if the L1-L2 phase changes are too high. Loss-of-lock is detected by comparing the estimated L2 C/N0 value against a threshold. After several Kalman filter tuning steps, the L2P signal is tracked down to low elevation angles. For example, the GPS Block IIF satellite PRN1 was tracked over a whole pass without cycle slips as shown in the code-minus-carrier plot in FIGURE 5. 

    Figure 5. Code minus carrier-phase measurements for GPS PRN1 at site GRAB on day of year 106, 2012.
    Figure 5. Code minus carrier-phase measurements for GPS PRN1 at site GRAB on day of year 106, 2012.

    One of the key applications of a professional GNSS receiver is its use as a GNSS reference station. Using a software receiver for this purpose would also provide increased monitoring capabilities to detect (un)intentional inference via RF spectral analysis or to detect signal anomalies due to satellite failures or multipath. Furthermore, it is useful for a number of scientific experiments and research topics such as scintillation monitoring or atmospheric occultation studies.

    Other GNSS Signals

    The inclusion of new GNSS signals in a software receiver is typically straightforward. The PRN codes need to be loaded and the tracking parameters (such as carrier frequency, integration time, error correction scheme, phase relation of signal components data/pilot, correlator positions, and discriminator type) can be selected without source code modification. If a hand-over from a different signal is performed (such as from GPS L1 to GPS L5) and if the first signal has already been synchronized to the transmit time by decoding the time-of-week, then it is possible to directly resolve the code ambiguity of the new signal. If this is not possible, a navigation message decoder has to be implemented to retrieve the time-of-week, which mostly has to be in the source code.

    Galileo AltBOC. An important exception to this rule is the Galileo AltBOC signal due to its high bandwidth. A conventional view on the AltBOC signal processing would require a sample rate of at least two times the total signal bandwidth. Depending on how many outer AltBOC side lobes are considered, this results in a sampling rate of 102 megasamples per second or more. This is undesirable for a cost-efficient software receiver solution, regarding the data transfer and the CPU load. The AltBOC processing inside the SX-NSR relies on the fact that both frequency bands E5a and E5b are sampled coherently and this coherency can be exploited to reconstruct the full AltBOC signal. The accuracy of the AltBOC navigation signal is concentrated in the main BOC sidelobes itself. More specifically, the thermal noise and multipath performance are dependent on the Gabor bandwidth, which represents the curvature of the correlation function at the tracking point. Thus a similar Gabor bandwidth can be obtained by sampling the E5a and the E5b band separately. This is the key idea of the resulting AltBOC processing scheme.

    The E5a and E5b signal samples are generated synchronously inside the same ADC chip and are transferred via the USB bus to the PC running the SX-NSR. The SX-NSR first acquires and tracks the signal separately on E5a and E5b. As it is quite efficient to run the E5a and E5b tracking on separate threads (and on separate CPU cores), the combination of E5a and E5b correlation values to E5 correlation values is done at the post-correlation level.

    There is no feedback from the E5 channel to the E5a/b channels. The channel maintains its own numerically controlled oscillator (NCO). A dedicated transformation is used to account for NCO differences between the E5a/b NCO values and the E5 NCO values. It is basically a sinc-interpolation in the code-phase direction and accounts for Doppler and carrier-phase differences. The transformed correlation values are added and yield an approximation to the AltBOC correlation function.

    The E5 correlation values are used to compute the discriminator values to update the E5 tracking loops. The E5 NCO values are used to compute the code pseudoranges and carrier-phase measurements, the Doppler frequency, and the C/N0 values, which are the primary outputs of the E5 receiver. Although the E5 receiver is a somehow a virtual receiver (that is, without correlators), it has the same user interface including most of the configuration parameters, output (for example, multi-correlator), and API.

    With AltBOC tracking, the Galileo satellites deliver code and phase measurements on five different carrier frequencies. A code-minus-carrier plot is shown in FIGURE 6. The code accuracy of the AltBOC signal is striking. The E6 signal is severely impacted by the present interference, and phase tracking is only possible for higher elevation angles.

    Figure 6. Code minus carrier-phase measurements for Galileo PRN12 at site GRAB on day of year 104, 2012.
    Figure 6. Code minus carrier-phase measurements for Galileo PRN12 at site GRAB on day of year 104, 2012.

    Polyfit and Vector Tracking

    A software receiver should provide a transparent way to retrieve pseudorange measurements that is well understood and can be well modeled. It should also provide a flexible input to control tracking NCO values. Both points are important if the receiver is part of larger navigation system (such as an integrated GNSS/INS system). Conventional delay-lock loop (DLL) / frequency-lock loop (FLL) / phase-lock loop (PLL) configuration is one option and is well understood by all GNSS researchers and engineers. It has, however, two major drawbacks. The loops introduce time correlations that cannot be easily modeled in a positioning Kalman filter, especially if low bandwidths (carrier aiding) are used. Second, the internal parameters of a DLL are difficult to match to a deeply coupled GPS/INS system.

    One way to overcome this is a method called polyfit tracking based on a rather old Jet Propulsion Laboratory patent (U.S. Patent No. 4821294). The idea behind this is to decouple pseudorange determination from the NCO counters. This is accomplished by forming the pseudoranges at the integrate-and-dump rate (such as 50 Hz) and to add the discriminator values to them. The resulting pseudorange is then obtained via a polyfit over the measurement interval.

    The time correlation of the measurements is solely determined by the discriminator values, and they compensate for the NCO correlations. A nice example is the application of this method to vector tracking. In vector tracking the NCO values are determined via a line-of-sight projection of the last position, velocity, and time (PVT) estimate and this estimate is usually slightly delayed. Furthermore, the line-of-sight projection is not perfect due to inevitable modeling errors (such as atmospheric delay errors). Thus the NCO does not follow the received signal as well as for DLL/FLL/PLL tracking. This is not a problem as the difference is captured in the discriminator values. FIGURE 7 shows the output of the method for a measurement interval of 0.5 second, one GPS C/A-code signal and for a dynamic user. The PVT update happens with a delay of about 100 milliseconds, changing the Doppler frequency. This resulting phase slope discontinuity is nicely compensated by the phase discriminator. The actual measurements are marked as brown stars in Figure 7. The method can also be applied to slave a channel to a master channel. This is useful for reflectometry, for example, where the master channel locks onto a line-of-sight signal and the slave channel tracks the reflected signal from sea surface.

    Figure 7. NCO-based phases (green) plus discriminator values (yellow) and polyfit for carrier-phase, code, and Doppler tracking (dynamic user, GPS C/A-code tracking).
    Figure 7. NCO-based phases (green) plus discriminator values (yellow) and polyfit for carrier-phase, code, and Doppler tracking (dynamic user, GPS C/A-code tracking).

    With multiple correlators (for example, nine correlators equally spaced from -0.5 to 0.3 chip for GPS C/A-code tracking), the polyfit method can be extended in a natural way to estimate and mitigate multipath. Using the polyfit carrier estimate, the multi-correlator values are coherently combined over the measurement interval and then a correlation function model is fitted to it. An eventually presented data bit is estimated and wiped off. The correlator fit starts with the assumption that only the line-of-sight signal is present. If the chi-squared value is above a certain threshold, the correlator fit is repeated assuming additionally one multipath signal. Up to two multipath signals can be estimated.

    The performance of this method can be tested with an RF signal generator. The scenario includes the line-of-sight signal (GPS C/A-code) and one multipath signal. The initial multipath delay is 0 meters and increases slowly (5.7 millimeters per second). The standard tracking method uses a multipath-mitigating double-delta code discriminator formed from four correlators (-0.2, -0.1, 0.1, 0.2) and an arctan carrier discriminator. Standard tracking is used to control the NCO values. FIGURE 8 shows that multipath is detected for delays larger than 15 meters. The detection performance depends on the carrier-phase difference of the line-of-sight and multipath signal, but for delays larger than 32 meters, multipath is always detected. If multipath is detected, the corrected ranges and C/N0 values are significantly improved.

    Figure 8. SX-NSR real-time carrier-phase multipath detection and mitigation performance for a GPS C/A-code signal with a -10 dB multipath signal (standard tracking shown in black, multipath-estimating discriminator output shown in red).
    Figure 8. SX-NSR real-time carrier-phase multipath detection and mitigation performance for a GPS C/A-code signal with a -10 dB multipath signal (standard tracking shown in black, multipath-estimating discriminator output shown in red).

    The polyfit method is used routinely in the reference station and has also been tested in a dynamic scenario. A bus drive near the IFEN office in Poing, Germany, with the antenna mounted on the roof has been carried out. Even in this rural area, short-term shading and multipath severely distort single channel (DLL/PLL) tracking causing rather large position errors (red dots in FIGURE 9).

    Source: Thomas Pany, Nico Falk, Bernhard Riedl, Tobias Hartmann, Günter Stangl, and Carsten Stöber
    Source: Thomas Pany, Nico Falk, Bernhard Riedl, Tobias Hartmann, Günter Stangl, and Carsten Stöber

    With a simple switch in the software, the NCO control can be switched from DLL/PLL to vector tracking (polyfit tracking is always on with the same fit parameters). If the standard point positioning (SPP) solution is used to control the NCO values (yellow dots), the errors are already drastically reduced because the NCOs follow the position and not the reflected signals. Also, erratic NCO jitter preceding loss-of-lock events is now eliminated. A further improvement is achieved if the PVT solution is computed by a Kalman filter (green dots), giving finally the typical high-navigation accuracy of modern GNSS receivers even with partial signal blocking.

    Dual-Antenna Heading Determination

    The bus drive mentioned above has actually been carried out with two antennas on the roof top with the aim of demonstrating the dual-antenna performance of the software receiver to determine heading. Two synchronized NavPorts have been used, both receiving GPS C/A-code signals (more frequencies would even be more beneficial and possible, but such a test has not yet been carried out). The software is fully prepared to handle data streams from two antennas and one option is to use the same NCO for both antennas. That is, the master antenna data is used to realize vector tracking and the discriminators of the slave channels capture the relative movement of the slave antenna to the master antenna. Again, polyfit tracking provides a natural framework to cope with this data.

    Attitude is determined with receiver single-difference observations. It is beneficial to form this difference as early as possible in the receiver processing, that is, immediately after correlation. Thus carrier-phase tracking is based on receiver single-difference correlators, being the product of the complex-conjugate master prompt correlator and the slave prompt correlator (both obviously for the same GNSS signal). The heading is shown in FIGURE 10. As reference, a GPS/INS system was used that calibrated the IMU during the first 300 seconds. One sees that the polyfit plus difference correlator is able to track the carrier phase continuously over 400 seconds in the rural test drive, although there is high multipath and some shading even for the high-elevation-angle satellites. Switching off only one option (vector tracking or the difference correlator) introduces cycle slips and corrupts the heading solution.

    Figure 10. Heading and heading error for the dual-antenna test.
    Figure 10. Heading and heading error for the dual-antenna test.

    Conclusions

    Currently, we see two main applications for software receivers. First, they may replace hardware receivers if the increased software receiver performance/flexibility justifies the increased power consumption and size. Several features have been shown in this article, and the possibility to do post-processing and the high-power CPU for customized algorithms are striking arguments for software receivers. On the other hand, software receivers may be customized by inserting user-specific code via the API offering enormous possibilities.

    Acknowledgments

    The research leading to the AltBOC results and the difference correlator results has received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement numbers 248151 and 247866, respectively. This article is based, in part, on the award-winning paper “Wide-band Signal Processing Features for Reference Station use of a PC-based Software Receiver: Cross-correlation Tracking on GPS L2P, AltBOC and the Inter-frontend Link for up to Eight Frequency Bands” presented at ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, held in Portland, Oregon, September 19–23, 2011.

    Manufacturers

    The IFEN GmbH NavPort/SX-NSR receiver at station GRAB is fed by a Leica Geosystems AG LEIAR25.R4 antenna with a LEIT radome. The kinematic test used a NovAtel Inc. SPAN GNSS/inertial system.

    THOMAS PANY works for IFEN GmbH in Poing, Germany, as a senior research engineer in the GNSS receiver department. He also works as a lecturer (Priv.-Doz.) at the Universität der Bundeswehr München (UniBwM) in Munich, Germany. NICO FALK works for IFEN GmbH in the receiver technology department. BERNHARD RIEDL works for IFEN GmbH as product manager for SX-NSR. TOBIAS HARTMANN works for IFEN GmbH in the receiver technology department. GÜNTER STANGL is an officer of the Austrian Federal Office for Metrology and Surveying and works half time at the Space Research Institute of the Austrian Academy of Sciences. CARSTEN STÖBER is a research associate at UniBwM.

     

    FURTHER READING

    • Authors’ Proceedings Paper

    “Wide-band Signal Processing Features for Reference Station Use of a PC-based Software Receiver: Cross-correlation Tracking on GPS L2P, AltBOC and the Inter-frontend Link for up to Eight Frequency Bands” by T. Pany, N. Falk, B. Riedl, T. Hartmann, J. Winkel, and G. Stangl in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 19–23, 2011, pp. 753–766.

    • IFEN Software Receiver Website

    • Overviews of Software GNSS Receivers

    “Real-Time Software Receivers: Challenges, Status, Perspectives” by M. Baracchi-Frei, G. Waelchli, C. Botteron, and P.-A. Farine in GPS World, Vol. 20, No. 9, September 2009, pp. 40–47.

    “GNSS Software Defined Radio: Real Receiver or Just a Tool for Experts?” by J.-H. Won, T. Pany, and G. Hein in Inside GNSS, Vol. 1, No. 5, July–August 2006, pp. 48–56

    “Satellite Navigation Evolution: The Software GNSS Receiver” by G. MacCougan, P.L. Normark, and C. Ståhlberg in GPS World, Vol. 16, No. 1, January 2005, pp. 48–55.

    • Software GNSS Receiver Algorithms and Implementations

    Digital Satellite Navigation and Geophysics: A Practical Guide with GNSS Signal Simulator and Receiver Laboratory by I.G. Petrovski and T. Tsujii with foreword by R.B. Langley, published by Cambridge University Press, Cambridge, U.K., 2012.

    “Simulating GPS Signals: It Doesn’t Have to Be Expensive” by A. Brown, J. Redd, and M.-A. Hutton in GPS World, Vol. 23, No. 5, May 2012, pp. 44–50.

    Navigation Signal Processing for GNSS Software Receivers by T. Pany, published by Artech House, Norwood, Massachusetts, 2010.

    A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach by K. Borre, D.M. Akos, N. Bertelsen, P. Rinder, and S.H. Jensen, published by Birkhäuser, Boston, 2007.

    “GNSS Radio: A System Analysis and Algorithm Development Research Tool for PCs” by J.K. Ray, S.M. Deshpande, R.A. Nayak, and M.E. Cannon in GPS World, Vol. 17, No. 5, May 2006, pp. 51–56.

    Fundamentals of Global Positioning System Receivers: A Software Approach, 2nd Edition, by J. B.-Y. Tsui, published by John Wiley & Sons, Inc., Hoboken, New Jersey, 2005.

    • Galileo Signal Tracking

    “Performance Evaluation of Single Antenna Interference Suppression Techniques on Galileo Signals using Real-time GNSS Software Receiver” by A.S. Ayaz, R. Bauernfeind, J. Jang, I. Kraemer, D. Dötterbock, B. Ott, T. Pany, and B. Eissfeller in Proceedings of ION GNSS 2010, the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 21–24, 2010, pp. 3330–3338.

    • Detecting Multipath and Signal Anomalies

    “Implementing Real-time Signal Monitoring within a GNSS Software Receiver” by C. Stöber, F. Kneißl, I. Krämer, T. Pany, and G. Hein in Proceedings of ENC-GNSS 2008, Toulouse, April 23–25, 2008.

    • International GNSS Service

    “The International GNSS Service in a Changing Landscape of Global Navigation Satellite Systems” by J.M. Dow, R.E. Neilan, and C. Rizos in Journal of Geodesy special issue, “The International GNSS Service (IGS) in a Changing Landscape of Global Navigation Satellite Systems,” Vol. 83, Nos. 3-4, 2009, pp. 191–198, doi: 10.1007/s00190-008-0300-3.

    “The International GNSS Service: Any Questions?” by A.W. Moore in GPS World, Vol. 18, No. 1, January 2007, pp. 58–64.

    IGS Multi-GNSS Experiment (M-GEX) website: http://www.igs.org/mgex.

    Software receiver data archive for site GRAB: ftp://olggps.oeaw.ac.at/pub/igsmgex/.

     

     

     

     

  • Innovation: The Devil Is in the Details

    Innovation: The Devil Is in the Details

    Looking Closely at Received GPS Carrier Phase

    By Johnathan York, Jon Little, and David Munton

    The stability of a received GPS signal determines how well the receiver can track the signal and the accuracy of the positioning results it provides. While the satellites use a very stable oscillator and modulation system to generate their signals, just how stable are the resulting phase-modulated carriers? In particular, do received signals always conform to the published system specifications? In this month’s column we take a look at a specially designed receiver for analyzing GPS carrier phase and some of the interesting results that have been obtained.

    GPS World photo
    INNOVATION INSIGHTS by Richard Langley

    A RADIO WAVE, OR ANY ELECTROMAGNETIC WAVE FOR THAT MATTER, may be generally characterized by four parameters: amplitude, frequency, phase, and polarization. If the values of amplitude, frequency, and polarization remain constant, then the wave is a pure oscillation or “tone” and can be represented as a sine wave.

    An unvarying tone doesn’t convey any information. However, the wave can be modulated by varying one or more of its characteristic parameters in a controlled fashion. In this way information, whether it be audio, images, or data, can be transmitted from one place to another. The sine wave is therefore referred to as a “carrier” (of the modulation). A continuous wave is a wave that is not interrupted.

    Of course, radio waves are not only used for communicating. They’re also used for navigation, radar, and many other purposes including the jamming of other radio signals. The modulating signal may either be continuously varying (analog) or have a fixed number of values of one or more of the parameters (digital) — two values in the case of binary modulation.

    Amplitude modulation is commonly used for broadcasting and communications. If a continuous wave is interrupted by keying the transmitter on and off using a code of some kind, such as Morse code, information can be sent. For speech and music transmission, an audio waveform is modulated onto the carrier.

    Frequency modulation is used for very high frequency (VHF) high-fidelity broadcasts and for communications in the VHF and ultra-high-frequency ranges of the radio spectrum. The instantaneous carrier frequency changes with the frequency and amplitude of the modulating waveform.

    Phase modulation is typically used for data transmissions and, as we know, this is how the pseudorandom noise codes and the navigation message modulate the signal carriers of GPS and other global navigation satellite systems. (While the polarization of a wave can be modulated to transmit information, this is not very common.) The stability of a received GPS signal — both the carrier and its modulations — determines, in part, how well the receiver can track the signal and the accuracy of the positioning results it provides.

    While the satellites use a very stable oscillator and modulation system to generate their signals, just how stable are the resulting phase-modulated carriers? In particular, do received signals always conform to the published system specifications? In this month’s column we take a look at a specially designed receiver for analyzing GPS carrier phase and some of the interesting results that have been obtained.

    “Innovation” features discussions about advances in GPS technology, its applications, and the fundamentals of GPS positioning. The column is coordinated by Richard Langley, Department of Geodesy and Geomatics Engineering, University of New Brunswick.

    By Johnathan York, Jon Little, and David Munton

    All global navigation satellite systems (GNSS) rely on well-defined data messages modulated onto stable carrier signals. The transmission of signals that adhere to published interface specifications (ISs) is what permits a GPS or GLONASS signal to be transmitted from a satellite and to be decoded at our receiver. This process is one that most of us never need to consider, and is part of the background magic that make GNSS so powerful.

    Still, signals are generated and received by real hardware — hardware that can be subject to the harsh space environment or a challenging ground environment. And once these signals are generated, they propagate to the user along a path through a dynamic medium that includes the ionosphere — a dilute plasma that introduces a well-known time-delay and phase change into the signal. The net result is is an effect on the signal that depends on both time and space.

    An interesting question is the following: How do we know that the signal we plan to send (as documented in an IS) is actually the signal that we receive? A pragmatic answer is that GNSS positioning works. If there is a difference between the IS-defined signal and the received signal, the impact is not seen by most users. Another answer is that satellite vendors test (and then test again) their equipment prior to launch, providing a high level of certainty that the ISs are being adhered too. In this article, we will describe our work in providing a third way of answering the question — by monitoring signals — motivated by our desire to see “all the bits, all the time.” We have seen some interesting effects in our observations, and we will discuss our attempts to detect and characterize these effects.

    Background

    For our purposes, we will be looking strictly at the L1 C/A-code signal. The reasons for this will become clear shortly. The standard textbook form of the noiseless signal is

      (1)

    where P is the signal power, cCA(t) is the C/A-code modulation stream of plus and minus ones, nNav(t) is the navigation bitstream that is modulated onto the signal, and the cos(ωt) factor represents the fundamental carrier frequency, with ω being the angular frequency (ω=2πf). For the GPS L1 signal, f = 1575.42 MHz. The GPS receiver processes this signal (in the presence of noise) into the observables (such as range, phase, or Doppler frequency shift), or the positions and velocities that we need.

    One of the research problems that we find interesting is determining how to monitor the details of the signal in Equation (1) or of any other GNSS signal. Why would this be of interest? To us this is interesting because we have seen events where the signal does not behave as expected. In fact, these events were first noted by the Federal Aviation Administration’s (FAA’s) Wide Area Augmentation System (WAAS) receivers, and were later noted again in ionospheric observations. By being able to monitor the signal at a very detailed level, we can hope to gain insight into the origins of these events.

    We are not alone in wanting to validate that the signal and data being produced by a GNSS receiver is valid. A standard approach to monitoring the GNSS signal would be to use an autonomous receiver method, known as receiver autonomous integrity monitoring or RAIM. However, in this approach, the integrity of the navigation solution is evaluated based on the range and phase observables produced by the receiver, and we obtain no insight into the behavior of the actual signal — only the receiver’s behavior in processing the received signals. Another option is to directly observe each satellite’s signal using a high-gain antenna. This approach provides significant insight into the behavior of the signal but is expensive and is really only effective on one satellite at a time. A system, which is close in spirit to our approach, is the Ohio University GPS Anomalous Event Monitor (GAEM). GAEM consists of two high-quality commercial receivers, which serve as independent triggers for an RF capture system. When the receivers detect an anomaly, the RF capture system is able to provide 20 seconds of raw RF data for study.

    Using an Inexpensive Software Receiver

    The observations we will discuss in the rest of this paper were made using what we term the Global Navigation Satellite System Complex Ambiguity Function receiver, or GCAF. The GCAF is a prototype receiver, and is well suited to some of the detailed analysis we have described.

    Briefly, the GCAF receiver is a single-channel, single-frequency (L1) GPS receiver, which uses firmware installed on a field programmable gate array (FPGA) to process the incoming GPS signal. FIGURE 1 is a labeled photograph of the GCAF. RF down-conversion occurs in the module at lower left. The down-converted signal is passed to an FPGA-based software receiver, shown at lower right. All of the processing to produce the complex correlation curves is done in the software receiver. The aggregator, shown at upper right, simply provides an Ethernet interface to the outside.

    By Johnathan York, Jon Little, and David Munton
    FIGURE 1. The GCAF receiver.

    The incoming signal is correlated against a replica of the expected L1 C/A-code signal, generating samples of the correlation curve. The difference between the GCAF and many standard commercial GPS receivers is that the GCAF samples the C/A-code correlation curve at 512 points (lags) at a 1-kHz rate. Each correlation sample is complex, consisting of in-phase (I) and quadrature (Q) components, with the software that processes the receiver raw data designed to maintain the signal in the I-component, and noise in the Q-component. As a result, the GCAF engine not only tracks the signal where it is expected to appear, but also at nearby offset phases and Doppler shifts simultaneously, and this ability substantially eliminates dependence on the tracking loop behavior and allows the observation of the characteristics of the received signal, rather than inferring them from observations of tracking loop behavior. See the sidebar, for more details on the receiver’s operation.

    Since the GCAF provides access to the high-rate complex correlation values, we can “decode” the navigation modulation sequence, nNav(t), from the incident signal by tracking the correlation peak phase and watching for phase changes. These phase changes correspond to distinct changes in the carrier phase. FIGURE 2 shows results from measurements collected with the GCAF while observing space vehicle number (SVN) 26 / pseudorandom noise code number (PRN) 26 on August 22, 2009. The top plot shows the amplitude of the in-phase component of the incident signal in blue, and that of the quadrature component in red. The amplitude is in arbitrary units, while the time along the bottom is in milliseconds–so the entire snapshot is only 0.6 seconds long.

    By Johnathan York, Jon Little, and David Munton
    FIGURE 2. Amplitude and phase of the detrended L1 C/A-code carrier of SVN26 (PRN26) recorded on August 22, 2009, at 10:16:30 GPS Time.

    These results in Figure 2 are as we expect, with the dominant energy appearing in the I-component. Clearly visible in the I-component is the navigation bitstream, which appears as a series of 180° phase changes in the carrier signal (hence changing the sign of the amplitude). The lower plot in Figure 2 shows the results of a “squaring” detector applied to the complex signal. Effectively this doubles any phase changes, since (ejφ)2 = ej(2φ). This nicely converts the navigation bitstream transitions to 2 × 180°, or 360°, which removes them from the signal. (This is the approach pioneered by one of the first commercial GPS receivers, the Macrometer, for providing correlation-free L1 phase observations by removing both the code and navigation message phase transitions.) What the lower plot in Figure 2 conveys is the absence of any transitions other than the expected ones of 180°.

    However, not all of our measurements are quite this typical. In some cases we observe what we term “carrier-phase signal events” (CPSEs). FIGURE 3 shows a typical example of such a CPSE taken on SVN48 (PRN21) on March 13, 2010. In the upper plot, note the sudden change in amplitude in the quadrature component near -100 milliseconds. In the lower plot, note the sudden changes in the carrier phase that occur at the same times as the amplitude changes. In this case, the squaring detector shows clear evidence of a transition that was not anticipated, and appears to be of approximately 90° and persist for approximately 175 milliseconds.

    By Johnathan York, Jon Little, and David Munton
    FIGURE 3. Decoded navigation bitstream on SVN45 (PRN21) taken on March 13, 2010, at 20:28:54 GPS Time.

    Of course, the single-channel nature of the GCAF does not permit an unambiguous identification of where in the signal chain a CPSE is introduced. The introduction of events might occur within the satellite transmission chain, or be produced within the propagation environment, or possibly be a quirk of the receiver itself. However, the types of events we observe seem a very unlikely failure mode for the GCAF. In the case of the example shown in Figure 2, the only place in the system where a signal at the exact Doppler-shifted frequency of the SV is in the numerically controlled oscillator (NCO) of the carrier-tracking loop. The GCAF tracking loop is updated at a rate slower than many of these events and manual examination of telemetry from the tracking loops in specific instances indicates no anomalous or discontinuous tracking behavior during the events examined. If events are generated by the local receiver environment, one possible mechanism would be a small multipath source at a position so as to induce a phase shift at a greater magnitude than the direct signal. This appears unlikely as events occur at many times of day (and therefore multipath geometries), and have onsets and durations that are difficult to explain with a reasonable multipath reflector.

    As a prototype instrument, the GCAF does have practical limitations. One of these limitations is that observations are divided into 5-minute intervals, at which point the signal is reacquired and data collected for another 5-minute interval. This is an operational limitation, which serves to improve robustness and bound individual output file sizes to 1 gigabyte each, and as a result, limits the durations of the CPSE that we can observe.

    Event Detection

    The simple squaring detector discussed above is not sufficient to provide a robust detection mechanism for the type of CPSEs we might see. In fact, we wanted a metric that would not rely on a pre-definition of what we might see in the signal, but which would flag changes in signal phase that might be interesting. To develop this metric, we borrowed ideas from the field of metrology, specifically work that characterizes noise types in oscillators. We ended up focusing on the modified Allan variance. While we will not detail the derivation of our metric here, we will discuss the results.

    The basic idea is to consider the phase, ϕ, of the GPS signal, averaged over sequential periods of duration τ. We choose τ to satisfy τ > 1 millisecond, since this is the basic chipping period of the L1 C/A-code signal. For the n-th period, τ, we denote this averaged phase by <ϕn>. By considering the impact of noise, specifically receiver thermal noise and clock stability, we can formulate a probabilistic bound of the form:

      (2)

    The interpretation of this result is that for a given averaging period τ the interval-to-interval variation in the average phase should never be too large. The right-hand side of Equation (2) provides a threshold for the phase variations over three consecutive periods, and is determined by the receiver thermal noise and clock stability. This bound, which is probabilistic in nature, applies with a false alarm rate of once in 10 years. If the metric exceeds this threshold, we declare that a phase event may have occurred within the three intervals.

    There is still the practical question of what averaging intervals τ need to be chosen. We have chosen to use a discrete set of τ that range from a few milliseconds to several seconds. This enables us to identify CPSEs that might occur rapidly (that is, at millisecond levels) or more slowly (at second levels). FIGURE 4 provides an example of the metric response to three consecutive CPSEs that are associated with SVN48 (PRN07). The upper plot shows the results of the squaring detector applied to the phase. Clearly evident are three rapid phase changes of about 20°. The next plot shows the result of the detection metric, which shows three double peaks in the vicinity of the phase changes. The third plot shows the I- (blue) and Q- (green) signal components. The bottom plot shows the NCO offset, which is a useful diagnostic.

    By Johnathan York, Jon Little, and David Munton
    FIGURE 4. A CPSE observed on SVN48 (PRN07) on September 15, 2010, at 19:21:42 GPS Time. (Click to enlarge.)

    Observations of Signal Events

    The examples we have shown so far reflect what we refer to as two-sided discontinuities; that is, a sudden change in phase, followed by a return to close to the original value. FIGURE 5 shows a similar type of CPSE, in which we only see one side of the change. We have seen this type of event quite commonly on SVN62 (PRN25). If there is a return to the original phase, it may be beyond our observation period. Note that the apparent slope in Figure 5 is an artifact of a linear detrending process acting across the discontinuity. FIGURE 6 shows an example of a different type of CPSE that we occasionally see, one in which a change in the slope of the phase occurs (corresponding to a change in frequency). The figure shows a single inflection in the phase rather than a rapid change in the phase value.

    FIGURE 5. A CPSE observed on SVN62 (PRN25) on January 16, 2011, at 16:26:03 GPS Time with a magnitude of about 40°. (Image: Authors)
    FIGURE 5. A CPSE observed on SVN62 (PRN25) on January 16, 2011, at 16:26:03 GPS Time with a magnitude of about 40°. (Image: Authors)
    By Johnathan York, Jon Little, and David Munton
    FIGURE 6. A CPSE observed on SVN38 (PRN08) on September 29, 2009, at 18:26:20 GPS Time. (Click to enlarge.)

    Over the entire GPS constellation, we see events with rapid phase changes most frequently associated with the signals from three SVNs: 45 (an original Block IIR satellite), 48 (a Block IIR-M satellite), and 62 (a Block IIF satellite). This is most clearly shown in FIGURE 7, which contains a histogram of the number of events with rapid phase changes we have seen, broken out by SVN. For this histogram, we have chosen to count only those events that have well-defined phase discontinuities. Other SVNs, for example SVN34 (a Block IIA satellite), will show CPSEs on occasion, but the signals from this set of three SVNs are the ones that we have come to observe most closely. Until recently, SVN62 was the newest SV, and so we have been heavily weighting our observations on this SV.

    FIGURE 7. Histogram of event counts for SVNs 45, 48, and 62 (PRNs 21, 07, and 25) covering the periods from mid-2009 until mid-August 2011. (Data: Authors)
    FIGURE 7. Histogram of event counts for SVNs 45, 48, and 62 (PRNs 21, 07, and 25) covering the periods from mid-2009 until mid-August 2011. (Data: Authors)

    Is There an Impact on Users?

    To conclude, it is worth assessing what the potential impact of signal events on user equipment might be. We first began to investigate the detailed carrier-phase structure when we learned that the FAA WAAS system found that the carrier phase from SVN45 behaved differently than the rest of the GPS constellation, and that similar effects were seen in SVN34 (PRN04) and SVN35 (PRN05). What was observed were short-duration irregularities (< 1 minute) in which the carrier phase changed rapidly. These events were noticed simultaneously across multiple receivers. These observations led to our use of the GCAF to investigate the carrier phase. It is clear that the CPSEs can have an impact on specialized equipment.

    But what about more standard user equipment? Given the types of events that we have observed, particularly those in which the phase changes suddenly and by a large amount, it is natural to ask how this might impact position and navigation users. A momentary 90-degree phase shift that lasts tens to hundreds of milliseconds might have varying effects on receivers depending on the duration of the event, the design of the carrier tracking loop in the receiver, and the instantaneous noise environment at each receiver.

    If the CPSE is shorter than the inverse of the receiver carrier tracking loop bandwidth, then the receiver might perceive the CPSE as a very brief loss of signal since the tracking loop will not be able to respond quickly enough. Observables formed from a second or more of raw values are likely to experience a small reduction in signal strength. As a result, short events are likely to go undetected by a traditional receiver that is primarily performing navigation.

    However, CPSEs that persist longer than the inverse of the receiver carrier-tracking-loop bandwidth could be interpreted by the receiver in a variety of ways, including a combination of cycle slip(s), navigation bit polarity inversion, or rapid carrier-phase changes.

    Summary

    We have been engaged in a detailed examination of the GPS L1 C/A-code signal for several years. In examining the signals, we have found that there are times when the signal exhibits an unexpected transition in phase. Looking across the GPS constellation, we find that these events tend to vary by satellite, both in rate and in behavior. While the impact from these events on most user equipment is small, the fact that the behavior is unique by SV is interesting. The type of detailed signal monitoring we have described is useful in two ways: it provides a means of observing effects that might otherwise pass unnoticed, and it gives us the capability to look for events in the future that might have a more obvious impact.

    Acknowledgment

    This article was stimulated by our research paper “A Non-Traditional Approach to Analysis of Signal Structure Anomalies Observed in PRN 21” presented at ION GNSS 2010, the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation in Portland, Oregon, September 21–24, 2010.

    Manufacturer

    The GCAF receiver uses a Xilinx, Inc., Spartan-3 FPGA.

    The Global Navigation Satellite System Complex Ambiguity Function Receiver

    The signal from the GCAF’s antenna passes through an amplifier stage, and then to an analog front end, where the signal is downconverted from the L1 frequency, 1575.42 MHz, directly to in-phase and quadrature IF signals. The signal is then passed to a Flexible Low-power Wideband Receiver (FLWR). The FLWR is a low-cost FPGA-based digitizing receiver designed and built by the Applied Research Laboratories at the University of Texas. Notably, the FPGA implementing the C/A-code replica generation and computation of the fast numeric theoretic transform (FNT) is an inexpensive 400 kilo-gate FPGA. The receiver is a two-channel, 10-bit, direct sample receiver, operating at 100 megasamples per second. The FLWR was built to operate as part of an array of antennas, and so connects to an aggregator. In the application discussed in this article, the aggregator simply serves as an interface between the receiver and a host computer. The C/A-code replica generator and the FNT computation of the correlation functions are written as Verilog firmware and loaded onto this receiver. Command and control and data collection occur over a USB port on the aggregator board, which is connected to a local computer.

    The host computer receives the time-domain correlation curves from the FPGA and stores them on disk for future processing. The time-domain correlation curve data is also processed by software in the host computer in order to provide feedback to the code and carrier local replica generators on the FPGA. In this way, the tracking loops are closed through the host computer via USB approximately every 100 milliseconds. Because the prototype GCAF provides hundreds of correlator output lags and a rapid dump period, the GCAF is able to track the peak very loosely. That is, unlike a traditional three-lag correlator, which must constantly track the correlation peak in order to produce meaningful data, the GCAF tracking loop needs remain only in the vicinity of the peak. Because the FNT-based GCAF is bit-accurate to traditional early/prompt/late correlators at each lag, there is potential to produce geodetic-quality observables in this loose tracking mode. This stands in contrast to the coarse quality typical of FFT-based loose-tracking approaches. In many cases, this property may make redundant the early/prompt/late-style correlator typically found alongside FFT-based correlators.

    Specifically, our prototype implementation has a sufficient number of correlator lags and a sufficiently high dump rate such that it is necessary to remain only within ±25 microseconds of the code peak and ±50 Hz of the carrier peak. The loose-tracking capability of GCAF has interesting implications for signal quality (and anomaly) monitoring. Commercially available atomic frequency standards have time drift rates of 0.2 microseconds per month, and absolute frequency accuracies of well below 1 Hz at the GPS L1 frequency. This level of accuracy means that the GCAF can perform open-loop tracking of GNSS signals when the receiver and satellite positions are known. Open-loop tracking is very useful for anomaly diagnosis and monitoring, as it observes the signals as received from the satellite, as opposed to observing their effects on a tracking loop.

    Johnathan York received a Ph.D. degree in electrical engineering from the University of Texas at Austin. He has worked at the University of Texas Applied Research Laboratories (ARL:UT) since 2001, working primarily with high-throughput real-time digital signal processing applications.

    Jon Little is a senior engineering scientist at ARL:UT. He holds a B.S. degree (1988) and an M.S. degree (1990) from Auburn University, Auburn, Alabama. He has worked extensively with the design and development of GPS ground systems and receivers.

    David Munton received a B.S. degree in physics from Sonoma State University in Rohnert Park, California, and a Ph.D. degree in physics from The University of Texas at Austin. He has worked as a research scientist at ARL:UT since 1993. His GNSS research interests include precise positioning and three-frequency measurement combinations.

    FURTHER READING

    ◾ Carrier-Phase Events and Monitoring

    “A Non-Traditional Approach to Analysis of Signal Structure Anomalies Observed in PRN 21” by J. Little, J. York, A. Farris, and D. Munton in Proceedings of ION GNSS 2010, the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 21–24, 2010, pp. 2190–2198.

    “Carrier-Phase Anomalies Detected on SVN-48” by B.W. O’Hanlon, M.L. Psiaki, S.P. Powell, and P.M. Kintner. Jr., in GPS World, Vol. 21, No. 6, June 2010, p. 27.

    “GNSS Watch Dog: A GPS Anomalous Event Monitor” by Z. Zhu, S. Gunawardena, M. Uijt de Haag, F. van Graas, and M. Braasch in Inside GNSS, Vol. 3, No. 7, Fall 2008, pp. 18–28.

    ◾ GCAF Receiver

    “A Fast Number-theoretic Transform Approach to a GPS Receiver” by J. York, J. Little, D. Munton, and K. Barrientos in Navigation: The Journal of The Institute of Navigation, Vol 57, No. 4, Winter 2010, pp. 297–307.

    “A Complex-Ambiguity Function Approach to a GPS Receiver” by J. York, J. Little, D. Munton, and K. Barrientos in Proceedings of ION GNSS 2009, the 22nd International Meeting of the Satellite Division of The Institute of Navigation, Savannah, Georgia, September 22–25, 2009, pp. 2637–2645.

    ◾ GPS Interface Specification

    Navstar GPS Space Segment / Navigation User Interfaces, Interface Specification, IS-GPS-200 Revision E, prepared by Science Applications International Corporation, El Segundo, California, for Global Positioning System Wing, June 2010.

    Global Navigation Satellite System GLONASS, Interface Control Document, Navigational Radio Signal in Bands L1, L2 (Edition 5.1), prepared by Russian Institute of Space Device Engineering, Moscow, 2008.

    ◾ Receiver Autonomous Integrity Monitoring

    “The Integrity of GPS” by R.B. Langley in GPS World, Vol. 10, No. 3, March 1999, pp. 60–63.

    ◾ GPS Signal Components

    “Minding Your Is and Qs” by R.B. Langley, a sidebar in “Open Source GPS–A Hardware/Software Platform for Learning GPS: Part II, Software” by C. Kelley and D. Baker in GPS World, Vol. 17, No.2, February 2006, p. 56.

    ◾ Modified Allen Variance

    “Allan Variance and Clock Stability” by R.B. Langley, a sidebar in “New IGS Clock Products: A Global Time Transfer Assessment” by J. Ray and K. Senior in GPS World, Vol. 13, No. 11, November 2002, p. 48.

    The Science of Timekeeping by D.W. Allan, N. Ashby, and C. Hodge, Agilent (formerly Hewlett-Packard) Application Note AN1289, Agilent Technologies Inc., Santa Clara, California, 1997 and 2000.

    Fig1
  • Innovation: Coming Soon

    Innovation: Coming Soon

    The International GNSS Real-Time Service

    By Mark Caissy, Loukis Agrotis, Georg Weber, Manuel Hernandez-Pajares, and Urs Hugentobler

    The International GNSS Service has embarked on a project to provide a high-accuracy GPS satellite orbit and clock data service in real time. The service will also provide 1-Hz data streams of GPS and GLONASS data from a network of global continuously operating reference stations. The IGS real-time data and orbit and clock products will be of immense benefit for geoscience studies and a host of other science and engineering applications. A team of authors associated with this project discusses the genesis and status of the real-time service and the plans to provide an initial operating capability.

    GPS World photo
    INNOVATION INSIGHTS by Richard Langley

    GPS HAS ALWAYS BEEN A REAL-TIME POSITIONING SYSTEM. From the outset, GPS was designed to provide virtually instantaneous position, velocity, and time, anywhere in the world, 24 hours per day. Its real-time positioning capability is achieved, in part, by measuring pseudoranges on multiple satellites simultaneously and by using the satellite orbit and clock data transmitted by the satellites themselves. The one-sigma accuracy of the horizontal component of the real-time positions obtained from measurements on the L1 frequency only, in a low multipath environment, can be as good as a meter. The accuracy is limited by the resolution and noise of the pseudorange measurements and the accuracy of the transmitted satellite orbit and clock data and the L1 ionospheric delay model.

    Much higher position accuracies are routinely achieved by using dual-frequency carrier-phase observations and precise satellite orbit and clock data computed from measurements provided by global tracking networks. Ionosphere corrections are also available for single-frequency users. The International GNSS Service (IGS) has been at the forefront of providing such data since its inception in 1994. The IGS now consists of over 200 actively contributing organizations in more than 80 countries and a global network of over 370 active stations. In addition to providing high-accuracy GPS satellite orbit and clock data, the IGS provides similar GLONASS products as well as GPS and GLONASS raw measurements and related information.

    Traditionally, the IGS data and products have been delivered with some delay with the intention that they be primarily used for so-called post-processing of user-collected data. For example, the “Final” GPS satellite orbit and clock products, the ones with highest accuracy, are delivered with a latency of 12–18 days. And while half of the “ultra-rapid” product is available for real-time use, the data is predicted based on earlier observations and has considerably less accuracy than the other IGS products. Recently, the IGS embarked on a project to provide a high-accuracy GPS satellite orbit and clock data service in real time. The service will initially also provide 1-Hz data streams of GPS and GLONASS data from a network of global continuously operating reference stations. Data and products from the Galileo and Compass systems will be added later. The IGS real-time data and orbit and clock products will be of immense benefit for geoscience studies and a host of other science and engineering applications.

    In this month’s column, a team of authors associated with this project discusses the genesis and status of the real-time service and the plans to imminently provide an initial operating capability.

    “Innovation” features discussions about advances in GPS technology, its applications, and the fundamentals of GPS positioning. The column is coordinated by Richard Langley, Department of Geodesy and Geomatics Engineering, University of New Brunswick.

    For more than a decade, the International GNSS Service (IGS) has been developing real-time infrastructure and processes and is now in the final stages of preparation for the launch of the IGS Real-Time Service (IGS-RTS) in the second half of 2012. The exact launch date will be decided at an IGS workshop in July. The service will begin with a status of initial operating capability (IOC) and will provide access to continuous streams of one-hertz GNSS data from a global network of stations in real time. It will also give access to globally valid wide-area GPS orbit and clock corrections, which will be capable of supporting sub-decimeter real-time precise point positioning (RTPPP).

    The availability of data and products from this new service will follow the IGS’s open data policy that these products will be openly available to all. Owing to the nature of this international collaboration, the IGS-RTS will be offered without a service guarantee. Data and products will be generated on a “best effort” basis; however, the service will have considerable redundancy built in and is likely to achieve the same degree of reliability for which other IGS services are known. When launched, the new service will contribute to the IGS goal of integrating new systems, technologies, and applications into IGS products and services so as to meet the changing needs of its user community.

    The IGS is an operational scientific service of the International Association of Geodesy and one of several services contributing to the Global Geodetic Observing System (GGOS). Data and products generated by the RTS will contribute to the natural hazards theme within GGOS. The RTS will support applications that detect, in real time, motions that are precursors to natural hazards such as landslides, volcanic activity, and tsunamis. To assist in fulfilling their own mandates, national geodetic and space agencies have contributed to the development of the real-time service and will continue to be involved both as contributors and users of the IGS real-time products. Other applications for the service will include GNSS constellation performance monitoring, weather forecasting, and space weather monitoring. For further background on the impact that real-time geodesy is having on the scientific community and applications, refer to the Eos article listed in the Further Reading sidebar. The online version of this article provides additional information in answer to the question “Why is the IGS involved in real-time GNSS?” (see also Further Reading).

    In this article, we discuss the following topics:

    • The open standards that have been adopted by the IGS for the delivery of real-time GNSS data, orbit, and clock corrections;
    • The IGS real-time infrastructure that is in place to ensure a reliable service;
    • The generation, organization, and performance of the real-time clock and orbit products;
    • A global real-time vertical total electron content (RT-VTEC) product under development;
    • User access and tools;
    • Future plans.
    Historical Look at Real Time

    The development of the RTS has followed the traditional stages of development for a new IGS service. First, a working group is formed and tasked with meeting certain goals. Second, a pilot project is initiated and, if successful, is followed by the third and final stage, which is the launch of the new service.

    The IGS Real-Time Working Group (RTWG) was established in 2001 with the goal of designing and implementing real-time infrastructure and processes for the delivery of real-time data to analysis centers, and the dissemination of real-time products to users. The working group’s direction was set at the IGS workshop, “Towards Real Time” held in Ottawa in the spring of 2002. At that time, the design for a prototype real-time service was adopted.

    In June 2007, the IGS announced the Call for Participation in the IGS Real-time Pilot Project with a three-year target to accomplish its goals. In 2009, the pilot project was extended to March 2011, and in August 2011 the working group declared that the pilot project had reached the additional goal of IOC and that it would be recommending to the IGS governing board the launch of an official real-time service.

    Open Standards Adopted

    An important objective of the IGS is to develop and maintain standards and formats for GNSS data and products. To achieve this objective for real-time GNSS, the IGS joined the Radio Technical Commission for Maritime Services Special Committee 104 (RTCM-SC104) in 2008. After joining RTCM, the IGS real-time project adopted the RTCM-3 format for GPS and GLONASS observation messages and the RTCM-State Space Representation (RTCM-SSR) format for orbit and clock correction messages.

    The Receiver Independent Exchange (RINEX) archival format became the shared responsibility of both the IGS and RTCM-SC104 in spring 2011. Because of this new development, there is now a project underway to develop binary messages that will enable the creation of a complete RINEX file from RTCM-SC104 binary messages. Part of this project involves the development of a new message format for GNSS data called RTCM-Multiple Signal Messages (RTCM-MSM). To enable interoperability among different GNSS receiver types, all phase observations in RTCM-MSM messages are aligned to the frequency band’s reference signal. An amendment to support the QZSS and Compass constellations is planned as the next step in the evolution of this format.

    IGS-RTS GNSS orbit and clock corrections are distributed using RTCM-SSR messages. These messages were designed to enable RTPPP and were officially adopted as an RTCM standard in May 2011. The format supports both GPS and GLONASS constellations. The combined resolution of the RTCM-SSR corrections supports millimeter-accuracy corrections and positioning at the same level. Enhancements to support Galileo, QZSS, and Compass constellations, and a global ionosphere correction format are planned.

    Delivery via NTRIP

    The IGS-RTS uses the Network Transport of RTCM by Internet Protocol (NTRIP) for internal operations and for the delivery of real-time products to its user community. NTRIP became an RTCM standard in 2004 and since that time has developed into a series of components that collectively provide a robust and proven system for the collection and distribution of GNSS information in real time. Being an RTCM standard, NTRIP is the ideal protocol for delivering and receiving HP-MSM and SSR messages.  More information on NTRIP can be found in Further Reading.

    Infrastructure Design

    Owing to the collaborative and best-effort nature of the contributions that collectively comprise each of its services, the IGS cannot make any commitments or guarantees for the accuracy or availability of the RTS. However, the IGS understands that its user community expects the service to be reliable, both in terms of accuracy and availability.

    To meet accuracy expectations, the IGS will strive to remain on the cutting edge of global real-time positioning and associated technologies as they evolve. To meet its user community’s expectations for availability of the service, the IGS will work to ensure there is a reliable flow of GNSS data and products from the source through the production chain, in real time without interruption. To accomplish this, redundancy has been provided for the paths across which data and products will flow, thus reducing the likelihood of total failure in the network.

    Figure 1 illustrates the distribution of real-time tracking stations in the network. The network is currently made up of approximately 130 globally distributed stations maintained by a wide variety of local and regional operators. These stations deliver one-hertz data to the real-time data centers with typical latencies of 3 seconds or less.

    Global coverage is essential for the success of the service, and the presence of redundant stations in geographical regions enhances the reliability of data available from these regions. This goal has been a challenge in some areas of the globe — for example, the south Pacific.
    IGS station operators are required to adhere to a minimum set of standards and are encouraged to adopt best practices for real-time operations.

    Examples of best practices are:

    • Real-time data should be transmitted to a minimum of two separate real-time data centers;
    • Stations that contribute to the realization of the IGS reference frame should be operated in real time to guarantee a reliable alignment of the real-time products to a stable reference frame.

    Real-time analysis centers (RTACs) are also encouraged to adopt the best practice of building the ability to ingest data from two or more global data centers into their processing strategy.

    Figure 2 illustrates the single tracking station and a regional network architecture. This arrangement specifies that data streams from the tracking stations should be sent to two separate real-time data centers where they become available to users. In this architecture, analysis centers can source reference station data from more than one data center. This design reduces the likelihood of single points of failure, making the data network more robust.

    Source: GPS
    Figure 2. GNSS station to data center architecture.

    Once the GNSS data are successfully delivered to the analysis centers, they are processed, the generated products are sent to combination centers, and the final product streams are distributed to users.

    Figure 3 illustrates the analysis-center to combination-center to user-network architecture. As with the classical orbit and clock products, the reliability of real-time products will be assured through the creation of a combined product that is based on submissions from a minimum of three RTACs. Analysis centers are encouraged to adopt the best practice of sending generated product streams to two independent combination centers. To ensure the availability of products, users will have redundant data centers from which to choose real-time products.

    Source: GPS
    Figure 3. IGS GNSS product distribution architecture.
    RTAC Design and Results

    As part of the Real-Time Pilot Project (RTPP), 11 RTACs generate real-time orbit and clock correction products:  the Federal Agency for Cartography and Geodesy (BKG); the Centre National d’Etudes Spatiales (CNES); the Czech Technical University (CTU); the German Aerospace Center (DLR); the European Space Operations Centre (ESOC); GEO++; the German Research Centre for Geosciences (GFZ); Natural Resources Canada (NRCan); GMV; the Vienna University of Technology (TUW); and Wuhan University (WUH).

    The design of the RTS specifies that GNSS orbit and clock corrections are to be delivered every 5 seconds. Typically RTACs wait 5 seconds for station data to be collected. Allowing 5 seconds for data processing and correction distribution yields a delay of 10 seconds once the RTAC products reach the combination center.

    The role of the real-time analysis center coordinator (RTACC), currently performed by ESOC, is to coordinate the activities of the RTACs and to generate and assess the quality of the combined real-time clock product. Table 1 shows snapshots of the performance of RTAC and combined products in the RTPP since 2009. The quality of the individual RTACs and the combined products is assessed through the root-mean-square (RMS) and standard deviation (sigma) of the difference between the individual products and the IGS rapid clock product. It is interesting to note the increase in participation as well as the improvement in the results over time. The target for the pilot project was to produce a combined clock product accurate to within 0.3 nanoseconds when compared to IGS rapid products. This was achieved early on in the project. The June 15, 2011, results shown are consistent with today’s results.

    Source: GPS
    Table 1. Real-time pilot project clock product comparisons.
    RTAC Coordinator Methods, Results

    The RTACs generate their orbit and clock estimates every 5 seconds and transmit them to the combination centers where they are processed using combination software. The latency of the combination process is 5 seconds, which, when added to the delay of products arriving from the individual RTACs, yields a total combination delay of approximately 15 seconds.

    The RTACC combination method detects and removes outliers that may be present in individual solutions. The combination is generated by first aligning all the solutions to a reference solution by removing a common solution-specific offset from all the satellite clocks. After alignment, clock differences between pairs of solutions are processed for outlier detection and for generation of a combination product. Satellite orbits are combined using solution averages after outlier detection.

    Satellite orbit corrections are estimated for two reference points, the satellite center of mass (CoM) and the satellite antenna phase center (APC). The orbit and clock correction products for both CoM and APC are encoded into RTCM-SSR streams. These streams are then transmitted to two or more data centers, where they become available to users or to other data centers. Additional information that will assist the user in selecting between CoM or APC streams will be available once the service is launched. Currently, only satellite orbit corrections referenced to APC are supported by the RTCM-SSR standard. To avoid confusion, the CoM streams will have restricted access when the IOC service is launched. The IGS will be tabling amendments to the RTCM standard in order to allow both reference points to be transmitted without restrictions.

    Table 2 shows combined product streams operating within the RTPP. Both a single-epoch combination product developed by ESOC and a Kalman-filter combined product developed collaboratively by BKG and CTU are available. A GPS-plus-GLONASS Kalman-filter combined product has also been developed at BKG and CTU.

    Source: GPS
    Table 2. Real-time IGS combination streams operating within the real-time pilot project.

    Figure 4 shows the history of the clock RMS performance of the single-epoch combination solution against the IGS “rapids.” This was the first combination product generated by the RTPP, and it started as a batch combination from daily orbit and clock file submissions by the RTACs. From early in 2010, ESOC started providing the first real-time combination product, generated directly by processing the real-time correction streams. The batch combination is in blue, while the real-time combination, starting in 2010, is in red. After an initial improvement phase, the results are stable except for occasional outliers. The outliers are due to problems in the individual solutions, and these should be removed by a properly executed combination methodology. Outliers in the combination towards the end of 2010 and beginning of 2011 were caused by RTCM encoding errors in some RTAC streams. Improvements to the outlier detection algorithm were introduced in early 2011, and it can be seen that the incidence of results with high RMS have been drastically reduced. Most outliers are now caused by poor orbit results after satellite maneuvers. Figure 5 illustrates the effectiveness of the outlier-detection algorithm.

    Source: GPS
    Figure 4. Combination solution clock performance. (Click to enlarge.)
    Source: GPS
    Figure 5. Combination solution performance with improved outlier detection.


    The RTAC orbit solutions use the predicted portion of an orbit arc. Most RTACs use the IGS Ultra Rapid Orbit product, but some use their own batch solutions, refreshed every one to two hours. The orbit results of the combined orbit product exhibit patterns similar to the clock results, with a significant improvement after outlier detection was introduced. The main problems are highlighted in Figure 6. There were some instances of what appear to be unannounced thrusting events on GPS satellite PRN 25. At times, problems arose from the re-introduction of previously unhealthy satellites. Other sources of error are occasional problems in one of the AC solutions, which are not entirely removed by the outlier detection algorithm.

    Source: GPS
    Figure 6. Combination solution orbit performance.

    The performance of the real-time combination products is monitored mainly through daily comparisons against the IGS rapid products as per the examples shown in Table 1. The products are also monitored through continuous kinematic precise point positioning (PPP) on the BKG NTRIP website. Sample combination stream PPP results are shown in Figure 7, where it can be seen that the horizontal error component is for the most part less than 10 centimeters and the vertical component is approximately a factor of two higher.

    Source: GPS
    Figure 7. Combination solution PPP performance of station FFMJ (Frankfurt, Germany) over 24 hours.

    Figure 8 illustrates the results of daily PPP convergence test conducted on the two GPS-only combination products. These are performed at 23 globally distributed sites during successive hours of the day. The results illustrated are the horizontal RMS errors for the last 10 minutes of each test, after an allowed convergence time of 50 minutes.

    Source: GPS
    Figure 8. Combination solution PPP convergence monitoring.
    Development of a RT-VTEC Product

    Within the IGS, associate analysis centers (ACC) produce specialized or derived products. Two examples of real-time ACCs are the Universitat Politècnica de Catalunya (UPC) and DLR. They have participated in the IGS RTPP and continue to collaborate on the development of a combined global IGS RT-VTEC product. This collaboration is occurring under the umbrella of the IGS Ionosphere Working Group led by the University of Warmia and Mazury in Olsztyn, Poland, the host for this summer’s IGS workshop.

    Figure 9 illustrates a comparison between preliminary global RT-VTEC products from UPC and DLR. This plot shows the RMS difference between each center’s product and Jason satellite altimeter VTEC measurements, taken over the ocean, versus the daily average number of active real-time GNSS receivers selected from the global real-time tracking network. A constant number of 80 stations was chosen for the DLR comparisons.  As a control for the comparisons, UPC’s rapid product (UQRG) was also used. The Jason comparisons are considered pessimistic for the overall global VTEC product accuracy because the land-based tracking stations are generally located quite far from the location of the Jason measurements. The importance of a reliable globally distributed and sufficiently dense real-time GNSS tracking network is evident. These results suggest that it may be feasible to combine real-time VTEC products from several centers into a robust IGS real-time ionosphere product.

    Work to compare both solutions is underway with the goal of finding optimal ways to assess and combine these products into an IGS RT-VTEC product. Future efforts will include working with RTCM to ensure that the IGS RT-VTEC product is compatible with ionosphere-correction information proposed for the RTCM-SSR standard.

    Source: GPS
    Figure 9. Comparisons between IGS real-time VTEC values and those from the Jason satellite altimeter.
    Products, User Access, and Tools

    Table 3 presents a list of products by category that will be offered by the service when it is launched. The list of products within each category will be finalized following the workshop in July. Once the final list of products is decided on, a user’s guide will be developed that will provide a detailed description of the products, their use, and where they can be accessed.

    Source: GPS
    Table 3. Initial products of the IGS Real-Time Service.

    It was mentioned earlier that the IGS-RTS uses the NTRIP protocol for the delivery of products to users. Users must use an NTRIP client application, either standalone or embedded in the user equipment, to establish a communication link with the data center that hosts the products of interest. Fortunately, open source software is available for this purpose: The BKG NTRIP Client (BNC) and the RTKLIB software (developed by T. Takasu) may be used. Both are open source applications and both support a variety of GNSS positioning applications. Links to these software packages are provided in Further Reading.

    Future Direction

    The real-time tracking network will continue to grow, and new receivers that can track all available GNSS constellation signals will be added. The IGS Multi-GNSS Experiment (M-GEX) will help improve the tracking network and associated data collection, quality control, and analysis procedures. Currently, several RTACs produce GLONASS orbits and clock corrections. Most RTACs are working to support the GLONASS and Galileo constellations, with QZSS and Compass on the horizon. The RTACC will continue to improve the combination process and reduce correction latency.  The availability of real-time data streams and corrections from several constellations will challenge the IGS and GNSS community to develop new and innovative applications that take advantage of all available GNSS observations and receiver hardware.

    Conclusion

    The IGS is now in the final stages of preparation for the launch of its Real-Time Service. As with other IGS services, the RTS will be offered without a service guarantee. From the initial formation of the RTWG in 2001, through the pilot project stage, to today’s state of readiness, the development of the service has benefited from collaboration among many member organizations, most notably the real-time analysis centers.
    Given its use of international standards, a built-in level of redundancy, and combined-products design, the IGS Real-Time Service will support robust high quality sub-decimeter real-time positioning on a global scale.

    Acknowledgments

    The authors wish to acknowledge the important contributions of the more than 30 agencies that participated in the IGS real-time pilot project. Most notably, the station operators and real-time analysis centers that we rely on to deliver, day in and day out, high quality data and products, and without whom the service would not be successful. The authors also wish to acknowledge the work of GEO++ in leading the development of the RTCM-SSR correction format.

    Why Is IGS Involved in Real-Time GNSS?

    Since its inception in 1994, the IGS has produced high-quality GNSS data products from a cooperative global infrastructure. The IGS products enable access to the definitive global reference frame for scientific, educational, and commercial applications that greatly benefit the public, and they are freely available to users.

    To date, access to this highly accurate reference frame has been ex post facto or predicted, limiting the utility of the IGS products. For years, IGS users have expressed a desire for real-time products to enhance existing applications, or to enable new applications that require low or no latency. This desire is now being satisfied by the IGS.

    Real-time GNSS has been an element of IGS strategy for more than 10 years in the context of providing innovative support for scientific applications and performance monitoring of GNSS. In 2002, the IGS conducted a cutting-edge workshop titled “Towards Real-Time,” which laid out a framework for developing a real-time service, from network configuration and management to algorithm development and product generation to definition of real-time protocols and standards.

    During this time, the IGS has faced many challenges. As technology has progressed to enable real-time GNSS applications, so has the perception that the IGS could become competitive with commercial entities, or even with IGS participants themselves. However, commercial services are generally not practical for users within sponsored research organizations, universities, national geodetic and mapping agencies, or non-governmental organizations because of costs imposed by for-profit business models, or a lack of technical transparency due to the proprietary nature of the services.

    The IGS response to these challenges is driven by a strong rationale to support public benefit applications. Principal beneficiaries include conventional weather and space weather forecasting, geophysical hazard detection and warning systems, and GNSS performance monitoring. Of key importance are real-time geophysical applications where openly available, global, real-time GNSS information is complementary to other information, such as seismic data, for rapidly detecting, locating, and characterizing hazardous events such as earthquakes and tsunamis.

    Quoting a 2011 article in the American Geophysical Union’s publication Eos, “…. Global Navigation Satellite System (GNSS) … provides an essential complement to other geophysical networks because of its high precision, sensitivity to the longest-period bands, ease of deployment, and ability to measure displacement and atmospheric properties over local to global scales. Recent and ongoing technical advances, combined with decreasing equipment and data acquisition costs, portend rapid increases in accessibility of data from expanding global geodetic networks. Scientists and the public are beginning to have access to these high-rate, continuous data streams and event-specific information within seconds to minutes rather than days to months.  These data provide the opportunity to observe Earth system processes with greater accuracy and detail, as they occur.”

    The IGS real-time products will include data streams from a global network of high-quality GNSS receivers, real-time combined orbits, accurate satellite clock solutions, and real-time ionosphere information. These products will enable real-time precise point positioning (PPP) at global scales for scientific and hazard detection applications. They will also have potential application for quality assessment of multi-constellation satellite performance and monitoring inter-system biases between the different GNSS.

    Mark Caissy is a team leader and senior geodetic engineer in the Geodetic Systems and Infrastructure Section of the Geodetic Survey Division, Natural Resources Canada. He chairs the International GNSS Service (IGS) Real-Time Working Group (RTWG) and the Real-Time Pilot Project (RTPP) Committee. His main interests are in the area of real-time precise point positioning for natural hazards monitoring.

    Loukis Agrotis, with his company Symban, is a contractor for the European Space Agency’s European Space Operations Centre working on the development of real-time GNSS infrastructure. He is the analysis center coordinator for the RTPP and represents the IGS at European meetings of the Radio Technical Commission for Maritime Services (RTCM). He holds a Ph.D., with dissertation title “Satellite Orbits and the Global Positioning System,” from the University of Nottingham, United Kingdom.

    Georg Weber is a scientific director in the Department of Geodesy at the German Federal Agency for Cartography and Geodesy (BKG), where he is responsible for the German National Reference System. As the major developer of Network Transport of RTCM by Internet Protocol, he also chairs the Internet Protocol Working Group in RTCM and is also a member of the IGS RTWG. He received his master’s degree and his Ph.D. in geodesy from the University of Hannover, Germany.

    Manuel Hernandez-Pajares is a full professor at the Universitat Politècnica de Catalunya in Barcelona, Spain. He served as chair of the IGS Ionosphere Working Group during the period 2002–2007.  He is currently working on new algorithms for precise ionospheric sounding and satellite navigation using GPS and Galileo data.

    Urs Hugentobler is a full professor of satellite geodesy at Technische Universität München, Munich, Germany, and the current chair of the IGS governing board. His main experience is in precise GNSS positioning applications and satellite orbit modeling.

    FURTHER READING

    • International GNSS Service

    Why is the IGS Involved in Real-time GNSS?

    “The International GNSS Service in a Changing Landscape of Global Navigation Satellite Systems” by J.M. Dow, R.E. Neilan, and C. Rizos in Journal of Geodesy special issue, “The International GNSS Service (IGS) in a Changing Landscape of Global Navigation Satellite Systems,” Vol. 83, Nos. 3-4, 2009, pp. 191–198, doi: 10.1007/s00190-008-0300-3.

    “The International GNSS Service: Any Questions?” by A.W. Moore in GPS World, Vol. 18, No. 1, January 2007, pp. 58–64.

    IGS publications web page: http://www.igs.org/overview/pubs.html

    • IGS Real-Time Service

    “IGS Real Time Infrastructure: From Pilot Project to Operational Service” by L. Agrotis, M. Caissy, G. Weber, M. Ge, K. MacLeod, and M. Hernández-Pajares, presented at the PPP-RTK and Open Standards Symposium, Frankfurt am Main, Germany, March 12-14, 2012.

    IGS Real-Time Pilot Project website: http://www.rtigs.net

    IGS-IP Ntrip Broadcaster website: http://www.igs-ip.net/home

    IGS-IP NTRIP Products Broadcaster: http://products.igs-ip.net/home

    • Real-time Data Generation and Delivery

    “Real-time Combination of GNSS Orbit and Clock Correction Streams Using a Kalman Filter Approach” by L. Mervart and G. Weber in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 19–23, 2011, pp. 707–711.

    “ESOC’s RETINA System and the Generation of the IGS RT Combination” by L. Agrotis, P. Alfaro Sanz, J. Dow, R. Zandbergen, D. Svehla, and A Ballereau, presented at the IGS Analysis Workshop, Newcastle, United Kingdom, June 28 – July 1, 2010.

    • Networked Transport of RTCM via Internet Protocol (NTRIP)

    “Real-time Clock and Orbit Corrections for Improved Point Positioning via NTRIP” by G. Weber, L. Mervart, Z. Lukes, C. Rocken, and J. Dousa in Proceedings of ION GNSS 2007, the 20th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, September 25–28, 2007, pp. 1992–1998.

    “Networked Transport of RTCM via Internet Protocol (Ntrip) …  IP-Streaming for Real-Time GNSS Applications” by G. Weber, D. Dettmering, H. Gebhard, and R. Kalafus in Proceedings of ION GNSS 2005, the 18th International Technical Meeting of the Satellite Division of The Institute of Navigation, Long Beach, California, September 13–16, 2005, pp. 2243–2247.

    NTRIP website: http://igs.bkg.bund.de/ntrip/index

    Open-source NTRIP software website: http://software.rtcm-ntrip.org

    Open-source GNSS positioning and NTRIP software website: http://www.rtklib.com

    • Precise Point Positioning

    “The CNES Real-time PPP with Undifferenced Integer Ambiguity Resolution Demonstrator” by D. Laurichesse in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 19–23, 2011, pp. 654–662.

    “Precise Point Positioning: A Powerful Technique with a Promising Future” by S.B. Bisnath and Y. Gao in GPS World, Vol. 20, No. 4, April 2009, pp. 43–50.

    “Online Precise Point Positioning: A New, Timely Service from Natural Resources Canada” by Y. Mireault, P. Tétreault, F. Lahaye, P. Héroux, and J. Kouba in GPS World, Vol. 19, No. 9, September 2008, pp. 59–64.

    • Ionospheric Modeling

    “A New Global TEC Model for Estimating Transionospheric Radio Wave Propagation Errors” by N. Jakowski, M.M. Hoque, and C. Mayer in Journal of Geodesy, Vol. 85, No. 12, 2011, pp. 965–974, doi: 10.1007/s00190-011-0455-1.

    “The Ionosphere: Effects, GPS Modeling and the Benefits for Space Geodetic Techniques” by M. Hernández-Pajares, J.M. Juan, J. Sanz, A. Aragón-Àngel, A. Garcia-Rigo, D. Salazar, and M. Escudero in Journal of Geodesy, Vol. 85, No. 12, 2011, pp. 887–907, 2011, doi: 10.1007/s00190-011-0508-5.

    “The IGS VTEC Maps: A Reliable Source of Ionospheric Information Since 1998” by M. Hernández-Pajares, J.M. Juan, J. Sanz, R. Orus, A. Garcia-Rigo, J. Feltens, A. Komjathy, S.C. Schaer, and A. Krankowski in Journal of Geodesy special issue, “The International GNSS Service (IGS) in a Changing Landscape of Global Navigation Satellite Systems,” Vol. 83, Nos. 3-4, 2009, pp. 263–275, doi: 10.1007/s00190-008-0266-1.

    • Doing Science with Real-Time GPS

    “Scientific Value of Real-Time Global Positioning System Data” by W.C. Hammond, B. A. Brooks, R. Bürgmann, T. Heaton, M. Jackson, A. R. Lowry, and S. Anandakrishnan in Eos, Vol. 92, No. 15, 2011, pp. 125–132, doi: 10.1029/2011EO150001.

  • Innovation: Simulating GPS Signals

    Innovation: Simulating GPS Signals

    It Doesn’t Have to Be Expensive

    By Alison Brown, Jarrett Redd, and Mark-Anthony Hutton

    GNSS signal simulators can be expensive and beyond the limited budgets of many researchers. In this month’s column, we look at one company’s approach to providing GNSS signal simulation at a low cost — one that virtually any researcher can afford.

    GPS World photo
    INNOVATION INSIGHTS by Richard Langley

    WHY DO WE SIMULATE REALITY  in mathematics, science, engineering, and other pursuits — even in our recreational activities? Well, we do it for a variety of reasons. In mathematics and science, we try to comprehend reality, which is complicated and variable and often has some degree of randomness. We build mathematical models of physical, chemical, or biological processes to better understand them or to predict a particular outcome given some initial conditions. The model may contain a stochastic component to reflect a degree of uncertainty associated with the processes. Weather forecasting is a prime example. Typically, the models are run on a computer where the model parameters and initial conditions can be readily adjusted and the varying outcomes analyzed.

    Simulations of reality are often used in teaching where students can more easily grasp the behavior of complicated systems whether they be in the natural sciences or in economics or the social sciences. In medical education, simulated human patients are used initially because it is safer than having students operate on real patients. Similarly, flight simulators are used for the training of pilots because it is cheaper and safer than using real aircraft and a wide variety of “what if” scenarios can be experienced.

    Simulation is used for a range of engineering activities to see how an existing system behaves under different conditions because it is faster or cheaper than performing tests in the “real world.” It can also be used to estimate how a proposed new system might behave before it becomes a reality — looking at traffic flow in road networks, for example.

    We also use simulation for recreation, whether it is playing with the latest computer game or improving our swing with a golf simulator. And simulation is a mainstay of the movie industry.

    But getting back to engineering and the main interest of this magazine, simulation is a useful technique in the design and operation of equipment used with global navigation satellite systems. With a radio frequency simulator, we can mimic the radio signals generated by the satellites. These devices allow us to define scenarios, including receiver trajectories, and to replay them while varying the operating parameters of the receiver. Some simulators allow us to record live signals and then to play them back under different assumed conditions.

    However, such GNSS signal simulators can be expensive and beyond the limited budgets of many researchers. In this month’s column, we look at one company’s approach to providing GNSS signal simulation at a low cost — one that virtually any researcher can afford.

    As the noted French sociologist and philosopher, Jean Baudrillard, pessimistically once said:  “We live in a world where there is more and more information, and less and less meaning.” In the field of GNSS engineering, at least, simulation is helping to stem the tide and give us a better understanding of reality.

    “Innovation” features discussions about advances in GPS technology, its applications, and the fundamentals of GPS positioning. The column is coordinated by Richard Langley, Department of Geodesy and Geomatics Engineering, University of New Brunswick. To contact him with topic ideas, email him at lang @ unb.ca.

    Embedded GPS receivers have become commonplace with the proliferation of GPS navigation systems into all but the least expensive vehicle and cell-phone lines. As more manufacturers embed low-cost GPS receivers into their products, the need for low-cost GPS signal simulators has also grown. Controlled virtual testing is vital in ensuring the expected system performance.

    Many GPS signal generators are available that are designed specifically for high-volume production test applications for devices that use commercial GPS/SBAS, GLONASS, and Galileo receivers. Often the cost of these high-end simulators is beyond the reach of small companies or universities. In response to this need, we have developed our low-cost, software-defined radio (SDR)-based GPS Signal Architect Test Set to address a broad range of research, academic, industrial, and defense applications. The system is designed to be flexible, scalable, and most importantly, inexpensive.

    Our test set leverages the capabilities of the Universal Software Radio Peripheral (USRP) radio and our GPS Signal Simulation Toolbox to provide users with a GPS signal generation capability at a much lower cost than currently available on the market. The combination of the GPS signal simulation software coupled with the record and playback capability of the USRP makes for an extremely low-cost, yet highly flexible, GPS signal simulation capability.

    FIGURE 1 shows the GPS signal simulator hardware. It is designed for use with commercial software-defined radios and is based on our GPS Signal Simulation Toolbox.

    Graphic: Alison Brown, Jarrett Redd, and Mark-Anthony Hutton
    FIGURE 1. GPS signal simulator hardware.

    Toolbox. The Toolbox is a complete set of GPS signal simulation, test, and analysis tools. This Matlab-based signal simulation toolbox simulates the effect of the signal degradation on a conventional commercial GPS receiver, including the effect of the ionospheric activity on the code and carrier tracking loops such as losing lock or cycle slipping. The Toolbox’s geographic tools facilitate the transformation of data between the various coordinate systems commonly used in GPS research, including latitude-longitude-altitude; WGS 84 Earth-centered, Earth-fixed (ECEF); north-east-down; and body-fixed reference frames. It also provides tools to read GPS almanacs and ephemerides and compute ECEF and line-of-sight vectors to GPS satellites as a function of user position and time. The receiver design and analysis tools model different receiver architectures and simulate different error scenarios by providing tracking and navigation algorithms, including phase lock loops and delay lock loops.

    The user-configurable options allow the operator to define virtually all aspects of a GPS signal environment, including the GPS spreading code(s), navigation message, and interference scenarios. Such flexibility is particularly useful in simulating GPS jamming environments, where time, resources, and repeatability are generally scarce.

    Because these tools are linked directly to Matlab, it is relatively simple to define and implement new signal components as they become available. Of primary interest are the GPS modernization codes as well as those of other global navigation satellite systems (GNSS). Also of interest are new and exotic categories of jammers, including frequency-modulated, amplitude-modulated, phase-modulated, and frequency-swept jammers.

    An early version of the toolbox was reviewed in a previous Innovation column (see Further Reading).

    Configuration. In the configuration shown in Figure 1, the signal control unit (SCU) is used to control the radio for record and playback operation. The USRP includes a 10-MHz frequency standard as well as an input for an external reference clock. The GPS Signal Architect software can produce custom GPS scenario data files, which can use the USRP to produce a GPS signal at RF.

    This article provides a review of how the signal simulator uses the USRP family of radios as low-cost RF record and playback devices using the Signal Architect files. In addition, the hardware design and supported signals are described and test results are presented showing the USRP providing simulated GPS signals to conventional GPS user equipment.

    Radio Hardware

    The USRP radio family provides an inexpensive development platform for software-defined radios. The USRP can also be used to record and play back the GPS signal in a static or mobile environment. The system operator can then reproduce the signal on the bench either from a simulated profile or from a previously recorded test environment. An advantage of the USRP is that it supports a wideband transceiver front-end that can accommodate the full 20 MHz of the GPS signal band and can be tuned to operate at any of the GPS signal frequencies (L1 at 1575.42 MHz, L2 at 1227.60 MHz, or L5 at 1176.45 MHz). This allows record and playback of both the civil and military GPS codes.

    While the GPS Signal Architect tools can be easily adapted for use with any commercial SDR, the USRP family was chosen because of its reasonable price, quality construction, and extensive support by the GNU Radio project.

    USRPs are SDRs, which can, in principle, transmit or receive signals on any frequency under software control. Typically, USRPs connect to a host computer through a high-speed USB or gigabit Ethernet link, which the host-based software uses to control the USRP hardware and to transmit or receive data. Some USRP models also integrate the general functionality of a host computer with an embedded processor that allows the USRP to operate in a standalone fashion. The USRP hardware is controlled through a hardware driver, which supports Linux, MacOS, and Windows platforms. A framework running on the host computer then accesses the USRP through the driver. Several frameworks, including GNU Radio (a free software toolkit for learning about, building, and deploying SDR systems developed under the GNU Project — “GNU” is a recursive acronym that stands for “GNU’s Not Unix”), LabView, Matlab, and Simulink, use the driver. The driver’s functionality can also be accessed directly with an application programming interface (API), which provides native support for C++. Any other language that can import C++ functions can also use the driver. This is accomplished in Python through the Simplified Wrapper and Interface Generator, for example. The API allows users to develop their own custom frameworks, as we did with our SCU.

    Of the available USRP radios, the N210 was chosen because it has the highest sample rate, greatest flexibility, and largest capacity for modification (see FIGURE 2).

    Graphic: Alison Brown, Jarrett Redd, and Mark-Anthony Hutton
    FIGURE 2. Univeral Software Radio Peripheral.

    The USRP provides an interface between high-speed analog to digital converters, high-speed digital to analog converters, and an Ethernet interface, as previously mentioned. Daughterboards available for the USRP provide an interface from the baseband signals present at the data converters to the GPS frequency bands and vice versa.

    A daughterboard (or daughtercard or piggyback board) is a circuit board meant to be an extension or “daughter” of a motherboard. The USRP uses interchangeable daughterboards, plugging into the main board, to serve as the RF front end. Several classes of daughterboard modules exist: receivers, transmitters, and transceivers. Transmitter daughterboard modules can modulate an output signal to a higher frequency; receiver daughterboard modules can acquire an RF signal and convert it to baseband; and transceiver daughterboard modules combine the functionality of a both a transmitter and receiver.

    For this project, a WBX (wide bandwidth) transceiver daughterboard was installed in the USRP. The tunable range of the WBX (50 MHz to 2.2 GHz) covers all the current GNSS frequencies. An RF pre-filter is used to band-limit the GNSS signals to the sample rate selected for use in the SCU to avoid spectral folding from the N210 40-MHz channel bandwidth. For example, a 2-MHz filter centered at L1 is optimal based on the Nyquist sampling frequency of 2 MHz of both the in-phase and quadrature (I/Q) components of the signal. If sampling at 20 MHz, then a 20-MHz pre-filter should be used.

    Signal Control Unit

    The SCU includes a Linux single-board computer with software developed to run under the GNU Radio Companion (an open-source graphical tool for creating signal flow graphs and generating flow-graph source code using the GNU Radio libraries) and management of the GNU SDR for RF record and playback under control of the GPS Signal Architect software through an Ethernet connection. This enables the user to tap into the excellent USRP community support for his or her project and benefit from the close relationship between the GNU Radio project and the USRP manufacturer. The Ethernet connection is also used to download and upload recorded or simulated signal files from the Signal Architect signal simulation software.

    Signal Simulation Software

    The GPS Signal Architect hardware and software provides users with a Matlab-based GPS signal generation capability. If our Matlab GPS Toolbox is provided, the Signal Architect GPS simulation can be run under the Matlab environment. For the non-Matlab user, the Signal Architect software is bundled as a stand-alone executable. The signal simulation flow is depicted in FIGURE 3.

    Graphic: Alison Brown, Jarrett Redd, and Mark-Anthony Hutton
    FIGURE 3. Signal Architect simulation flow. (Click to enlarge.)

    GUI. Using a simple, intuitive graphical user interface (GUI), the user specifies a trajectory and a complete set of simulation parameters to create an I/Q data file (see FIGURE 4). The user specifies a trajectory either from an NMEA file (most GPS receivers use the National Marine Electronics Association 0183 interface standard for logging positions and other data) or a KML file from Google Earth (Google’s Keyhole Markup Language has become a standard for describing geographically referenced features), and an almanac file used to define GPS satellites to be simulated. The user defines the mask angle for the satellite selection and the noise figure to be simulated. The Signal Architect software then generates a simulated digital storage file, including the selected pseudorandom noise codes (C/A and/or the unencrypted military P or M′ codes).

    Graphic: Alison Brown, Jarrett Redd, and Mark-Anthony Hutton
    FIGURE 4. Signal Architect graphical user interface. (Click to enlarge.)
    Graphic: Alison Brown, Jarrett Redd, and Mark-Anthony Hutton
    TABLE 1. Signal simulator system specifications.

    Scenarios. The Signal Architect software also ships with preloaded scenario files that the user can run right out of the box into the SCU using the USRP. The Signal Architect software supports computers with multi-core processors and will automatically configure itself to run on all available processors. The Signal Architect software will generate either static or dynamic simulation profiles. The Signal Architect GUI allows the operator to easily modify a wide range of scenario variables from the pre-set defaults. Complete scenarios are easily shared between signal simulation systems, supporting collaborative testing between similar projects and reducing the amount of time spent developing test tools.

    The signal data file can then be used for subsequent analysis within Matlab using the Matlab GPS Toolbox, or can be provided to the SCU and USRP to create a GPS signal suitable for playback into a GPS receiver. If the Matlab GPS Toolbox is available, the user has complete flexibility to manipulate the signal at various stages of generation or post-generation to simulate GPS anomalies. Without the toolbox, the user is restricted to using only the standard error modeling provided by the compiled Signal Architect code.

    Simulation Test Results

    To demonstrate the high fidelity of our Signal Architect signal record and playback capability, a series of stationary GPS simulations were run. In these tests, the USRP was used to record and play back GPS C/A-code signals at the L1 band (1575.42 MHz). The SCU and USRP were connected to a rooftop-mounted GPS L1 antenna. The GPS signal was split between a commercial GPS receiver and the USRP to allow the operator to monitor the GPS receiver while the USRP was recording the GPS signal.

    In record mode, the I/Q data is written from the USRP to a file on the SCU. In playback mode, the data is read from the file by the USRP to generate the RF signal. The RF signals are output to the GPS receiver through an external variable attenuator. The attenuator allows the operator to adjust the signal power into the GPS receiver as different lengths of antenna cable are added or as the signal is split to other GPS receivers.

    To demonstrate the GPS Signal Architect Test Set performance, representative data was collected in a series of two laboratory tests. The first test demonstrates the system performance as a record and playback GPS signal simulator. The second test results demonstrate the system performance when using the Signal Architect software to generate custom GPS scenario files for playback into the GPS receiver.

    In the first test, the GPS simulator hardware was configured as shown in FIGURE 5. The GPS receiver and USRP were connected to a commercially available antenna. The antenna was positioned at a known location with a clear view of the GPS constellation. The signal from the GPS antenna was split between the GPS receiver and the USRP so that the data could be logged by the receiver software at the same time as it was being recorded by the SCU.

    FIGURE 5. GPS Signal Simulator record and playback GPS simulation.
    FIGURE 5. GPS Signal Simulator record and playback GPS simulation.

    The simulated satellite constellation is shown in FIGURE 6. Seven GPS satellites are in view.

    Graphic: Alison Brown, Jarrett Redd, and Mark-Anthony Hutton
    FIGURE 6. Simulated satellite constellation.

    The 2-D position error from the simulated signal is shown in FIGURE 7. The errors are representative of the accuracies achievable using GPS C/A-code pseudoranges.

    Graphic: Alison Brown, Jarrett Redd, and Mark-Anthony Hutton
    FIGURE 7. North-east (2-D) position error.

    We can examine the performance of our test set by looking at plots of the measurements of carrier-to-noise-density ratio (C/N0) from the GPS receiver for both the live sky data and for the recorded signal when played into the GPS receiver by the Signal Simulator for three of the GPS receiver channels (see FIGURE 8). The C/N0 data collected from the GPS antenna is shown in blue, while the C/N0 from the USRP is shown in green.

    Graphic: Alison Brown, Jarrett Redd, and Mark-Anthony Hutton
    FIGURE 8. C/N0 record and playback vs. live sky collection.

    As we can see, the C/N0 was 1–2 dB lower in playback mode when compared to the data collected from the GPS antenna. The signal loss is due to the 1-bit sampling of the incoming GPS signal by the Signal Architect software. One-bit and 2-bit quantization are used in many commercial GPS receivers. The rule of thumb states that 1-bit quantization degrades the signal-to-noise ratio by 1.96 dB, and 2-bit quantization degrades the signal-to-noise by 0.55 dB. These results show that 1-bit I/Q sampling is sufficient for reproducing GPS L1 C/A-code signals with the USRP.

    In the second test, the Signal Architect software was used to generate a 10-minute static GPS C/A-code L1 scenario file. The SCU used the USRP to generate the GPS signal.

    Shown in FIGURE 9 are the number of satellites the GPS receiver was able to track. When using the GPS Signal Architect Test Set to play back the scenario file, the GPS receiver was able to track all the simulated satellites in the file. The time necessary for the GPS receiver to acquire and track the satellites is consistent with the performance one would expect from the GPS receiver when connected to an external antenna.

    FIGURE 10 shows the C/N0 measurements from the GPS receiver for three of the receiver channels. There were nine satellites in this static scenario file. The C/N0 for all the satellites is stable for the duration of the scenario playback.

    Graphic: Alison Brown, Jarrett Redd, and Mark-Anthony Hutton
    FIGURE 9. Number of satellites tracked (digital signal file playback mode).
    Graphic: Alison Brown, Jarrett Redd, and Mark-Anthony Hutton
    FIGURE 10. C/N0 (digital signal file playback mode).

    Another Hardware/Software Option

    We have also worked with the manufacturer of the LabSat hardware signal simulator to include some of the software functionality of our USRP system.

    The LabSat GNSS simulator (see FIGURE 11) can be used to record live navigation satellite RF data streamed onto a hard drive. This can then be played back as an RF signal. When integrated with the SatGen software, simulated digitized RF data can be generated and played back into the LabSat simulator in place of the recorded, digitized GNSS RF signals.

    Photo: Alison Brown, Jarrett Redd, and Mark-Anthony Hutton
    FIGURE 11. LabSat GNSS simulator.

    The core of the SatGen software is our Signal Architect software component, which has been adapted to run on the LabSat platform to allow simulation of the multiple GNSS satellite signals.

    Hardware. The latest version of the LabSat hardware design (LabSat 2) enables the record and playback of GPS and GLONASS synchronized RF data streams (see FIGURE 12). When recording the GPS or GLONASS signals, the RF L1 channels (1575.42 MHz for GPS and 1602.0 MHz for GLONASS) are down-converted to a 0-Hz intermediate frequency. The signals are sampled (2 bit I and Q) at 16.368 MHz to capture the full GLONASS set of frequency channels. The GPS and GLONASS data is then interleaved into a 4-bit data stream and recorded in an internal buffer as a binary file. A high-speed USB link then transmits the data to a PC before streaming onto the PC hard disk. For playback, the PC streams the stored binary data to the LabSat via the USB link. The recorded digital GPS and GLONASS signals are up-converted onto the GPS and GLONASS RF channels and played back into the receiver under test. A digital attenuator on the output can adjust the RF output level.

    Graphic: Alison Brown, Jarrett Redd, and Mark-Anthony Hutton
    FIGURE 12. LabSat GNSS simulator hardware design.

    Using the LabSat record and playback mode, all of the real-world effects on the GNSS signals are recorded, including multipath effects, drop-outs, and atmospheric effects, allowing repeatable tests to be performed on GNSS receivers under a variety of real-world conditions, such as operating in urban canyons. This is ideal for debugging fault conditions on GNSS equipment and software. For more extensive simulations in different environments, the LabSat SatGen software can be used to generate simulated scenarios at any time or place or for a dynamic environment.

    SatGen Scenario Simulation. SatGen is a software package that allows users to define trajectories for use in generating simulated data files for playback into LabSat. A user-defined trajectory file can be used to create a LabSat-simulated scenario for a route anywhere in the world. Routes can be generated directly from NMEA files imported directly into SatGen from a GPS datalogger or from user-defined routes generated using Google Earth.

    SatGen users can use Google Earth to define a route by creating a path using its “Add Path” tool. The user can use as many or as few waypoints as the user wants, and can edit routes by moving, adding, or removing waypoints. The path is saved as a standard Google Earth KML file, which is imported into SatGen, which then fills in and smoothes the trajectory between the waypoints. The user can also define velocity profiles, or SatGen can provide these automatically. SatGen creates an NMEA file that is used to generate a binary I/Q simulated signal file for replay on the LabSat hardware.

    Signal Architect Simulation. The core of the SatGen software is GNSS Signal Architect, an upgraded version of our GPS Signal Architect, which provides the capability to simulate multiple GPS signals and also different GNSS signals.

    Signal Architect imports the NMEA trajectory either from a prerecorded file or from one generated using SatGen, and uses this file to generate a GNSS-simulated scenario. The user specifies the input GPS satellite constellation through a Yuma-format almanac file and the GLONASS constellation through a GLONASS almanac file in “.agl” or RINEX format. These files are then used to generate the simulated pseudorange, Doppler, and carrier phase for the GPS and GLONASS satellites in view of the simulated GNSS receivers above a specified mask angle. This simulated range data is then used to generate the digitized I/Q signals for the GPS and GLONASS satellites. Users who have access to our GNSS Signal Simulation Toolbox (an upgrade of our GPS toolbox) will have the added ability to modify the GNSS signal strength and add additional high-resolution error models to the simulated signals including multipath or GNSS signal error characteristics. The resulting I/Q simulated data file for the GPS plus GLONASS constellation is then recorded in a data file, which can be loaded into the LabSat hardware for playback into a receiver under test.

    Test results using the LabSat and SatGen combination have demonstrated that highly accurate navigation solutions can be obtained with a variety of playback modes.

    Conclusion

    The combination of our GPS Signal Architect software with either the SCU and USRP or LabSat has proven to be an ideal low-cost GPS signal simulation tool with the capability of simulating or recording the complete GPS signal spectrum, including both the civil and the military codes for playback. The initial release of the GPS Signal Architect Test Set supports L1 operation and C/A- and P-code and M′ signal simulation or C/A- and P(Y)-code and M′ record and playback, while both GPS and GLONASS signal generation and playback is available with LabSat.

    Our team of GPS and RF experts is continually developing and updating the system to provide additional functionality. Future releases of our test set will include support for multi-frequency SDR hardware and the capability to simulate other civil and military GPS signals, and also those of other global navigation satellite systems. To reflect this capability, we have branded the latest version of our simulation system, the GNSS Signal Architect Test Set.

    Acknowledgments

    The authors acknowledge the support of Ettus Research LLC in the development of the technology associated with the USRP system, as well as Racelogic Ltd. for collaboration on the LabSat GNSS simulator. USRP is a registered trademark of National Instruments Corp.

    The article is based primarily on the papers “GPS Signal Simulation Using Open Source GPS Receiver Platform” presented at the Virginia Tech Symposium on Wireless Personal Communication in June 2011 and “SatGen GNSS Signal Simulation Software” presented at ION GNSS 2011 in Portland, Oregon, in September 2011.

    Manufacturers

    The GNSS Signal Architect Test Set was developed by Navsys Corp. The USRP used for the test set is the Ettus Research LLC model USRP N210. The LabSat 2 GNSS Simulator and associated SatGen software is produced by Racelogic Ltd. The GPS equipment used in our tests was a Novatel DL-4 plus receiver and a GPS-702GG antenna.

    Alison Brown is the president and chief executive officer of Navsys Corp., Colorado Springs, Colorado, which she founded in 1986. Brown has a Ph.D. in mechanics, aerospace, and nuclear engineering from UCLA, an M.S. in aeronautics and astronautics from MIT, and an M.A. and B.A. in engineering from Cambridge University. She is a fellow of the Institute of Navigation and an honorary fellow of Sidney Sussex College, Cambridge.

    Jarrett Redd is a senior systems engineer with Navsys Corp. working on hardware, firmware, and embedded systems development for signal acquisition, processing, and transmission. He holds an M.S. and B.S. in computer engineering from Texas A&M University.

    Mark-Anthony Hutton is a software engineer with Navsys Corp. working on GNSS signal simulation tools and the GPS Jammer Detection and Location System. He holds a B.S. in computer science from the University of Colorado at Colorado Springs.

    FURTHER READING

    • Authors’ Proceedings Papers

    “SatGen GNSS Signal Simulation Software” by A. K. Brown, M.-A. Hutton, M. Quigley, and M. Sampson in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 19–23, 2011, pp. 2031–2034.

    “GPS M’-Code and P-Code Signal Simulation Using an Open Source Radio Platform” by A. Brown and B. Johnson in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 19–23, 2011, pp. 1494–1498.

    “GPS Signal Simulation using Open Source GPS Receiver Platform” by A. Brown, R. Tredway, and R. Taylor in Proceedings of the 21st Virginia Tech Symposium on Wireless Personal Communications, Blacksburg, Virginia, June 1–3, 2011.

    “Modeling and Simulation of GPS Using Software Signal Generation and Digital Signal Reconstruction” by A. Brown, N. Gerein, and K. Taylor in Proceedings of the 2000 National Technical Meeting of The Institute of Navigation, Anaheim, California, January 26–28, 2000, pp. 646–652.

    • GNU Radio

    GNU Radio Wiki.

    Open Source Software-Defined Radio: A Survey on GNUradio and its Applications by D. Valerio, technical report, FTW-TR-2008-002, Forschungszentrum Telekommunikation Wien, Vienna, Austria, August 2008.

    “GNU Radio: Tools for Exploring the Radio Frequency Spectrum” by E. Blossom in Linux Journal, Issue No. 122, June, 2004.

    • GNSS Simulation

    “Simulating Inertial/GNSS Hybrid: SINERGHYS Test Bench for Military and Avionics Receivers” by S. Gallot, P. Dutot, and C. Sajous in GPS World, Vol. 23, No. 5, May 2012, pp. 38–43.

    “Realistic Randomization: A New Way to Test GNSS Receivers” by A. Mitelman in GPS World, Vol. 22, No. 3, March 2011, pp. 43–48.

    “Record, Replay, Rewind: Testing GNSS Receivers with Record and Playback Techniques” by D. A. Hall in GPS World, Vol. 21, No. 10, October 2010, pp. 28–34.

    “GNSS Simulation: A User’s Guide to the Galaxy” by I. Petrovski, T. Tsujii, J.-M. Perre, B.  Townsend, and T. Ebinuma in Inside GNSS, Vol. 5, No. 5, October 2010, pp. 52–61.

    “GPS Simulation” by M. B. May in GPS World, Vol. 5, No. 10, October 1994, pp. 51–56.

    • Matlab Simulation Toolboxes

    “GPS MATLAB Toolbox Review” by A.K. Tetewsky and A. Soltz in GPS World, Vol. 9, No. 10, October 1998, pp. 50–56.

    • NMEA 0183

    NMEA 0183, The Standard for Interfacing Marine Electronic Devices, Ver. 4.00, published by the National Marine Electronics Association, Severna Park, Maryland, November 2008.

    “NMEA 0183: A GPS Receiver Interface Standard” by R.B. Langley in GPS World, Vol. 6, No. 7, July 1995, pp. 54–57.

    Unofficial online NMEA 0183 descriptions: NMEA data; NMEA Revealed by E.S. Raymond, Ver. 2.8, February 2011.

  • Innovation: Ionospheric Scintillations

    Innovation: Ionospheric Scintillations

    How Irregularities in Electron Density Perturb Satellite Navigation Systems

    By the Satellite-Based Augmentation Systems Ionospheric Working Group

    GPS World photo
    INNOVATION INSIGHTS by Richard Langley

    THE IONOSPHERE. I first became aware of its existence when I was 14. I had received a shortwave radio kit for Christmas and after a couple of days of soldering and stringing a temporary antenna around my bedroom, joined the many other “geeks” of my generation in the fascinating (and educational) hobby of shortwave listening. I avidly read Popular Electronics and Electronics Illustrated to learn how shortwave broadcasting worked and even attempted to follow a course on radio-wave propagation offered by a hobbyist program on Radio Nederland. Later on, a graduate course in planetary atmospheres improved my understanding.

    The propagation of shortwave (also known as high frequency or HF) signals depends on the ionosphere. Transmitted signals are refracted or bent as they experience the increasing density of the free electrons that make up the ionosphere. Effectively, the signals are “bounced” off the ionosphere to reach their destination. 

    At higher frequencies, such as those used by GPS and the other global navigation satellite systems (GNSS), radio signals pass through the ionosphere but the medium takes a toll. The principal effect is a delay in the arrival of the modulated component of the signal (from which pseudorange measurements are made) and an advance in the phase of the signal’s carrier (affecting the carrier-phase measurements). The spatial and temporal variability of the ionosphere is not predictable with much accuracy (especially when disturbed by space weather events), so neither is the delay/advance effect. However, the ionosphere is a dispersive medium, which means that by combining measurements on two transmitted GNSS satellite frequencies, the effect can be almost entirely removed. Similarly, a dual-frequency ground-based monitoring network can map the effect in real time and transmit accurate corrections to single-frequency GNSS users. This is the approach followed by the satellite-based augmentation systems such as the Federal Aviation Administration’s Wide Area Augmentation System.

    But there is another ionospheric effect that can bedevil GNSS: scintillations. Scintillations are rapid fluctuations in the amplitude and phase of radio signals caused by small-scale irregularities in the ionosphere.  When sufficiently strong, scintillations can result in the strength of a received signal dropping below the threshold required for acquisition or tracking or in causing problems for the receiver’s phase lock loop resulting in many cycle slips.

    In this month’s column, the international Satellite-Based Augmentation Systems Ionospheric Working Group presents an abridged version of their recently completed white paper on the effect of ionospheric scintillations on GNSS and the associated augmentation systems.

    The ionosphere is a highly variable and complex physical system. It is produced by ionizing radiation from the sun and controlled by chemical interactions and transport by diffusion and neutral wind. Generally, the region between 250 and 400 kilometers above the Earth’s surface, known as the F-region of the ionosphere, contains the greatest concentration of free electrons. At times, the F-region of the ionosphere becomes disturbed, and small-scale irregularities develop. When sufficiently intense, these irregularities scatter radio waves and generate rapid fluctuations (or scintillation) in the amplitude and phase of radio signals. Amplitude scintillation, or short-term fading, can be so severe that signal levels drop below a GPS receiver’s lock threshold, requiring the receiver to attempt reacquisition of the satellite signal. Phase scintillation, characterized by rapid carrier-phase changes, can produce cycle slips and sometimes challenge a receiver’s ability to hold lock on a signal. The impacts of scintillation cannot be mitigated by the same dual-frequency technique that is effective at mitigating the ionospheric delay. For these reasons, ionospheric scintillation is one of the most potentially significant threats for GPS and other global navigation satellite systems (GNSS).

    Scintillation activity is most severe and frequent in and around the equatorial regions, particularly in the hours just after sunset. In high latitude regions, scintillation is frequent but less severe in magnitude than that of the equatorial regions. Scintillation is rarely experienced in the mid-latitude regions. However, it can limit dual-frequency GNSS operation during intense magnetic storm periods when the geophysical environment is temporarily altered and high latitude phenomena are extended into the mid-latitudes. To determine the impact of scintillation on GNSS systems, it is important to clearly understand the location, magnitude and frequency of occurrence of scintillation effects.

    This article describes scintillation and illustrates its potential effects on GNSS. It is based on a white paper put together by the international Satellite-Based Augmentation Systems (SBAS) Ionospheric Working Group (see Further Reading).

    Scintillation Phenomena

    Fortunately, many of the important characteristics of scintillation are already well known. 

    Worldwide Characteristics. Many studies have shown that scintillation activity varies with operating frequency, geographic location, local time, season, magnetic activity, and the 11-year solar cycle. FIGURE 1 shows a map indicating how scintillation activity varies with geographic location. The Earth’s magnetic field has a major influence on the occurrence of scintillation and regions of the globe with similar scintillation characteristics are aligned with the magnetic poles and associated magnetic equator. The regions located approximately 15° north and south of the magnetic equator (shown in red) are referred to as the equatorial anomaly. These regions experience the most significant activity including deep signal fades that can cause a GNSS receiver to briefly lose track of one or more satellite signals. Less intense fades are experienced near the magnetic equator (shown as a narrow yellow band in between the two red bands) and also in regions immediately to the north and south of the anomaly regions. Scintillation is more intense in the anomaly regions than at the magnetic equator because of a special situation that occurs in the equatorial ionosphere. The combination of electric and magnetic fields about the Earth cause free electrons to be lifted vertically and then diffuse northward and southward. This action reduces the ionization directly over the magnetic equator and increases the ionization over the anomaly regions. The word “anomaly” signifies that although the sun shines above the equator, the ionization attains its maximum density away from the equator.

    FIGURE 1 Global occurrence characteristics of scintillation. (Figure courtesy of P. Kintner) .Credit: the Satellite-Based Augmentation Systems Ionospheric Working Group
    FIGURE 1. Global occurrence characteristics of scintillation. (Figure courtesy of P. Kintner)

    Low-latitude scintillation is seasonally dependent and is limited to local nighttime hours. The high-latitude region can also encounter significant signal fades. Here scintillation may also accompany the more familiar ionospheric effect of the aurora borealis (or aurora australis near the southern magnetic pole) and also localized regions of enhanced ionization referred to as polar patches. The occurrence of scintillation at auroral latitudes is strongly dependent on geomagnetic activity levels, but can occur in all seasons and is not limited to local nighttime hours. In the mid-latitude regions, scintillation activity is rare, occurring only in response to extreme levels of ionospheric storms. During these periods, the active aurora expands both poleward and equatorward, exposing the mid-latitude region to scintillation activity. In all regions, increased solar activity amplifies scintillation frequency and intensity. Scintillation effects are also a function of operating frequency, with lower signal frequencies experiencing more significant scintillation effects. 

    Scintillation Activity. Scintillation may accompany ionospheric behavior that causes changes in the measured range between the receiver and the satellite. Such delay effects are not discussed in detail here but are well covered in the literature and in a previous white paper by our group (see Further Reading, available online).

    Amplitude scintillation can create deep signal fades that interfere with a user’s ability to receive GNSS signals. During scintillation, the ionosphere does not absorb the signal. Instead, irregularities in the index of refraction scatter the signal in random directions about the principal propagation direction. As the signal continues to propagate down to the ground, small changes in the distance of propagation along the scattered ray paths cause the signal to interfere with itself, alternately attenuating or reinforcing the signal measured by the user. The average received power is unchanged, as brief, deep fades are followed by longer, shallower enhancements. 

    Phase scintillation describes rapid fluctuations in the observed carrier phase obtained from the receiver’s phase lock loop. These same irregularities can cause increased phase noise, cycle slips, and even loss of lock if the phase fluctuations are too rapid for the receiver to track.

    Equatorial and Low Latitude Scintillations. As illustrated in Figure 1, the regions of greatest concern are the equatorial anomaly regions. In these regions, scintillation can occur abruptly after sunset, with rapid and deep fading lasting up to several hours. As the night progresses, scintillation may become more sporadic with intervals of shallow fading. FIGURE 2 illustrates the scintillation effect with an example of intense fading of the L1 and L2 GPS signals observed in 2002, near a peak of solar activity. The observations were made at Ascension Island located in the South Atlantic Ocean under a region that has exhibited some of the most intense scintillation activity worldwide. The receiver that collected this data was one that employs a semi-codeless technique to track the L2 signal. Scintillation was observed on both the L1 and L2 frequencies with 20 dB fading on L1 and nearly 60 dB on L2 (the actual level of L2 fading is subject to uncertainty due to the limitations of semi-codeless tracking). This level of fading caused the receiver to lose lock on this signal multiple times. Signal fluctuations depicted in red indicate data samples that failed internal quality control checks and were thereby excluded from the receiver’s calculation of position. The dilution of precision (DOP), which is a measure of how pseudorange errors translate to user position errors, increased each time this occurred. In addition to the increase in DOP, elevated ranging errors are observed along the individual satellite links during scintillation. 

    FIGURE 2 Fading of the L1 and L2 Signals from one GPS satellite recorded from Ascension Island on March 16, 2002. Absolute power levels are arbitrary. (Figure courtesy of C. Carrano) . Credit: the Satellite-Based Augmentation Systems Ionospheric Working Group
    FIGURE 2. Fading of the L1 and L2 Signals from one GPS satellite recorded from Ascension Island on March 16, 2002. Absolute power levels are arbitrary. (Figure courtesy of C. Carrano)

    FIGURE 3 illustrates the relationship between amplitude and phase scintillations, also using measurements from Ascension Island. As shown in the figure, the most rapid phase changes are typically associated with the deepest signal fades (as the signal descends into the noise). Labeled on these plots are various statistics of the scintillating GPS signal: S4 is the scintillation intensity index that measures the relative magnitude of amplitude fluctuations, τI is the intensity decorrelation time, which characterizes the rate of signal fading, and σφ is the phase scintillation index, which measures the magnitude of carrier-phase fluctuations.

    FIGURE 3 Intensity (top) and phase scintillations (bottom) of the GPS L1 signal recorded from Ascension Island on March 12, 2002. (Figure courtesy of C. Carrano) . Credit: the Satellite-Based Augmentation Systems Ionospheric Working Group
    FIGURE 3. Intensity (top) and phase scintillations (bottom) of the GPS L1 signal recorded from Ascension Island on March 12, 2002. (Figure courtesy of C. Carrano)

    The ionospheric irregularities that cause scintillation vary greatly in spatial extent and drift with the background plasma at speeds of 50 to 150 meters per second. They are characterized by a patchy pattern as illustrated by the schematic shown in FIGURE 4. The patches of irregularities cause scintillation to start and stop several times per night, as the patches move through the ray paths of the individual GPS satellite signals. In the equatorial region, large-scale irregularity patches can be as large as several hundred kilometers in the east-west direction and many times that in the north-south direction. The large-scale irregularity patches contain small-scale irregularities, as small as 1 meter, which produce scintillation. Figure 4 is an illustration of how these structures can impact GNSS positioning. Large-scale structures, such as that shown traversed by the signal from PRN 14, can also cause significant variation in ionospheric delay and a loss of lock on a signal. Smaller structures, such as those shown traversed by PRNs 1, 21, and 6, are less likely to cause loss of the signal, but still can affect the integrity of the signal by producing ranging errors. Finally, due to the patchy nature of irregularity structures, PRNs 12 and 4 could remain unaffected as shown. Since GNSS navigation solutions require valid ranging measurements to at least four satellites, the loss of a sufficiently large number of satellite links has the potential to adversely affect system performance. 

    FIGURE 4. Schematic of the varying effects of scintillation on GPS.
    FIGURE 4. Schematic of the varying effects of scintillation on GPS.

    FIGURE 5 illustrates the local time variation of scintillations. As can be seen, GPS scintillations generally occur shortly after sunset and may persist until just after local midnight. After midnight, the level of ionization in the ionosphere is generally too low to support scintillation at GNSS frequencies. This plot has been obtained by cumulating, then averaging, all scintillation events at one location over one year corresponding to low solar activity. For a high solar activity year, the same local time behavior is expected, with a higher level of scintillations.

    FIGURE 5 Local time distribution of scintillation events from June 2006 to July 2007 (in 6 minute intervals). (Figure courtesy of Y. Béniguel) .Credit: the Satellite-Based Augmentation Systems Ionospheric Working Group
    FIGURE 5. Local time distribution of scintillation events from June 2006 to July 2007 (in 6 minute intervals). (Figure courtesy of Y. Béniguel)

    FIGURE 6 (top panel) shows the variation of the monthly occurrence of scintillation during the pre-midnight hours at Ascension Island. The scintillation data was acquired by the use of Inmarsat geostationary satellite transmissions at 1537 MHz (near the GNSS L1 band). The scintillation occurrence is illustrated for three levels of signal fading, namely, > 20 dB (red), > 10 dB (yellow), and > 6 dB (green). The bottom panel shows the monthly sunspot number, which correlates with solar activity and indicates that the study was performed during the years 1991 to 2000, extending from the peak of solar cycle 22 to the peak of solar cycle 23. Note that there is an increase in scintillation activity during the solar maximum periods, and there exists a consistent seasonal variation that shows the presence of scintillation in all seasons except the May-July period. This seasonal pattern is observed from South American longitudes through Africa to the Near East. Contrary to this, in the Pacific sector, scintillations are observed in all seasons except the November-January period. Since the frequency of 1537 MHz is close to the L1 frequencies of GPS and other GNSS including GLONASS and Galileo, we may use Figure 6 to anticipate the variation of GNSS scintillation as a function of season and solar cycle. Indeed, in the equatorial region during the upcoming solar maximum period in 2012-2013, we should expect GNSS receivers to experience signal fades exceeding 20 dB, twenty percent of the time between sunset and midnight during the equinoctial periods. 

    FIGURE 6 Frequency of occurrence of scintillation fading depths at Ascension Island versus season and solar activity levels. (Figure courtesy of P. Doherty) . Credit: the Satellite-Based Augmentation Systems Ionospheric Working Group
    FIGURE 6. Frequency of occurrence of scintillation fading depths at Ascension Island versus season and solar activity levels. (Figure courtesy of P. Doherty)

    High Latitude Scintillation. At high latitudes, the ionosphere is controlled by complex processes arising from the interaction of the Earth’s magnetic field with the solar wind and the interplanetary magnetic field. The central polar region (higher than 75° magnetic latitude) is surrounded by a ring of increased ionospheric activity called the auroral oval. At night, energetic particles, trapped by magnetic field lines, are precipitated into the auroral oval and irregularities of electron density are formed that cause scintillation of satellite signals. A limited region in the dayside oval, centered closely around the direction to the sun, often receives irregular ionization from mid-latitudes. As such, scintillation of satellite signals is also encountered in the dayside oval, near this region called the cusp.

    When the interplanetary magnetic field is aligned oppositely to the Earth’s magnetic field, ionization from the mid-latitude ionosphere enters the polar cap through the cusp and polar cap patches of enhanced ionization are formed. The polar cap patches develop irregularities as they convect from the dayside cusp through the polar cap to the night-side oval. During local winter, there is no solar radiation to ionize the atmosphere over the polar cap but the convected ionization from the mid-latitudes forms the polar ionosphere. The structured polar cap patches can cause intense satellite scintillation at very high and ultra-high frequencies. However, the ionization density at high latitudes is less than that in the equatorial region and, as such, GPS receivers, for example, encounter only about 10 dB scintillations in contrast to 20-30 dB scintillations in the equatorial region.

    FIGURE 7 shows the seasonal and solar cycle variation of 244-MHz scintillations in the central polar cap at Thule, Greenland. The data was recorded from a satellite that could be viewed at high elevation angles from Thule. It shows that scintillation increases during the solar maximum period and that there is a consistent seasonal variation with minimum activity during the local summer when the presence of solar radiation for about 24 hours per day smoothes out the irregularities. 

    FIGURE 7 Variation of 244-MHz scintillations at Thule, Greenland with season and solar cycle. (Figure courtesy of P. Doherty) . Credit: the Satellite-Based Augmentation Systems Ionospheric Working Group
    FIGURE 7. Variation of 244-MHz scintillations at Thule, Greenland with season and solar cycle. (Figure courtesy of P. Doherty)

    The irregularities move at speeds up to ten times larger in the polar regions as compared to the equatorial region. This means that larger sized structures in the polar ionosphere can create phase scintillation and that the magnitude of the phase scintillation can be much stronger. Large and rapid phase variations at high latitudes will cause a Doppler frequency shift in the GNSS signals which may exceed the phase lock loop bandwidth, resulting in a loss of lock and an outage in GNSS receivers.

    As an example, on the night of November 7–8, 2004, there was a very large auroral event, known as a substorm. This event resulted in very bright aurora and, coincident with a particularly intense auroral arc, there were several disruptions to GPS monitoring over the region of Northern Scandinavia. In addition to intermittent losses of lock on several GPS receivers and to phase scintillation, there was a significant amplitude scintillation event. This event has been shown to be very closely associated with particle ionization at around 100 kilometers altitude during an auroral arc event. While it is known that substorms are common events, further studies are still required to see whether other similar events are problematic for GNSS operations at high latitudes. 

    Scintillation Effects 

    We had mentioned earlier that the mid-latitude ionosphere is normally benign. However, during intense magnetic storms, the mid-latitude ionosphere can be strongly disturbed and satellite communication and GNSS navigation systems operating in this region can be very stressed. During such events, the auroral oval will extend towards the equator and the anomaly regions may extend towards the poles, extending the scintillation phenomena more typically associated with those regions into mid-latitudes. 

    An example of intense GPS scintillations measured at mid-latitudes (New York) is shown in FIGURE 8. This event was associated with the intense magnetic storm observed on September 26, 2001, during which the auroral region had expanded equatorward to encompass much of the continental U.S. This level of signal fading was sufficient to cause loss of lock on the L1 signal, which is relatively rare. The L2 signal can be much more susceptible to disruption due to scintillation during intense storms, both because the scintillation itself is stronger at lower frequencies and also because semi-codeless tracking techniques are less robust than direct correlation as previously mentioned.

    FIGURE 8 GPS scintillations observed at a mid-latitude location between 00:00 and 02:00 UT during the intense magnetic storm of September 26, 2001. (Figure courtesy of B. Ledvina) . Credit: the Satellite-Based Augmentation Systems Ionospheric Working Group
    FIGURE 8. GPS scintillations observed at a mid-latitude location between 00:00 and 02:00 UT during the intense magnetic storm of September 26, 2001. (Figure courtesy of B. Ledvina)

    Effects of Scintillation on GNSS and SBAS

    Ionospheric scintillation affects users of GNSS in three important ways: it can degrade the quantity and quality of the user measurements; it can degrade the quantity and quality of reference station measurements; and, in the case of SBAS, it can disrupt the communication from SBAS GEOs to user receivers. As already discussed, scintillation can briefly prevent signals from being received, disrupt continuous tracking of these signals, or worsen the quality of the measurements by increasing noise and/or causing rapid phase variations. Further, it can interfere with the reception of data from the satellites, potentially leading to loss of use of the signals for extended periods. The net effect is that the system and the user may have fewer measurements, and those that remain may have larger errors. The influence of these effects depends upon the severity of the scintillation, how many components are affected, and how many remain.

    Effect on User Receivers. Ionospheric scintillation can lead to loss of the GPS signals or increased noise on the remaining ones. Typically, the fade of the signal is for much less than one second, but it may take several seconds afterwards before the receiver resumes tracking and using the signal in its position estimate. Outages also affect the receiver’s ability to smooth the range measurements to reduce noise. Using the carrier-phase measurements to smooth the code substantially reduces any noise introduced. When this smoothing is interrupted due to loss of lock caused by scintillation, or is performed with scintillating carrier-phase measurements, the range measurement error due to local multipath and thermal noise could be from three to 10 times larger. Additionally, scintillation adds high frequency fluctuations to the phase measurements further hampering noise reduction.

    Most often scintillation will only affect one or two satellites causing occasional outages and some increase in noise. If many well-distributed signals are available to the user, then the loss of one or two will not significantly affect the user’s overall performance and operations can continue. If the user has poor satellite coverage at the outset, then even modest scintillation levels may cause an interruption to their operation. When scintillation is very strong, then many satellites could be affected significantly. Even if the user has excellent satellite coverage, severe scintillation could interrupt service. Severe amplitude scintillation is rarely encountered outside of equatorial regions, although phase effects can be sufficiently severe at high latitudes to cause widespread losses of lock.

    Effect on Reference Stations. The SBAS reference stations consist of redundant GPS receivers at precisely surveyed locations. SBAS receivers need to track two frequencies in order to separate out ionospheric effects from other error sources. Currently these receivers use the GPS L1 C/A-code signal and apply semi-codeless techniques to track the L2 P(Y) signal. Semi-codeless tracking is not as robust as either L1 C/A or future civil L5 tracking. The L2 tracking loops require a much narrower bandwidth and are heavily aided with scaled-phase information from the L1 C/A tracking loops. The net effect is that L2 tracking is much more vulnerable to phase scintillation than L1 C/A, although, because of the very narrow bandwidth, L2 tracking may be less susceptible to amplitude scintillation. Because weaker phase scintillation is more common than stronger amplitude scintillation, the L2 signal will be lost more often than L1. The SBAS reference stations must have both L1 and L2 measurements in order to generate the corrections and confidence levels that are broadcast. Severe scintillation affecting a reference station could effectively prevent several, or even all, of its measurements from contributing to the overall generation of corrections and confidences. Access to the L5 signal will reduce this vulnerability. The codes are fully available, the signal structure design is more robust, and the broadcast power is increased. L5-capable receivers will suffer fewer outages than the current L2 semi-codeless ones, however strong amplitude scintillation will still cause disruptions. Strong phase scintillation may as well.

    If scintillation only affects a few satellites at a single reference station, the net impact on user performance will likely be small and regional. However, if multiple reference stations are affected by scintillation simultaneously, there could be significant and widespread impact.

    Effect on Satellite Datalinks. The satellites not only provide ranging information, but also data. When scintillation causes the loss of a signal it also can cause the loss or corruption of the data bits.

    Each GPS satellite broadcasts its own ephemeris information, so the loss of data on an individual satellite affects only that satellite. A greater concern is the SBAS data transmissions on GEOs. This data stream contains required information for all satellites in view including required integrity information. If the data is corrupted, all signals may be affected and loss of positioning becomes much more likely.

    Mitigation Techniques. There are several actions that SBAS service providers can take to lessen the impact of scintillation. Increasing the margin of performance is chief among them. The more satellites a user has before the onset of scintillations, the more likely he will retain performance during a scintillation event. In addition, having more satellites means that a user can tolerate more noise on their measurements. Therefore, incorporating as many satellites as possible is an effective means of mitigation. GNSS constellations in addition to GPS are being developed. Including their signals into the user position solution would extend the sky coverage and improve the performance under scintillation conditions. (See the white paper for other mitigation techniques.)

    Conclusions and Further Work

    Ionospheric scintillations are by now a well-known phenomenon in the GNSS user community. In equatorial regions, ionospheric scintillations are a daily feature during solar maximum years. In auroral regions, ionospheric scintillations are not strongly linked to time of the day. In the mid-latitude regions, scintillations tend to be linked to ionospheric disturbances where strong total electron content gradients can be observed (ionospheric storms, strong traveling ionospheric disturbances, solar eclipses, and so on). 

    While the global climatic models of ionospheric scintillations can be considered satisfactory for predicting (on a statistical basis) the occurrence and intensity of scintillations, the validation of these models is suffering from the fact that at very intense levels of scintillation, even specially designed scintillation receivers are losing lock. Also, the development of models that can predict reliably the size of scintillation cells (regions of equal scintillation intensity), which allows establishing joint probabilities of losing more than one satellite simultaneously, is still ongoing.

    Acknowledgments

    This article is based on the paper “Effect of Ionospheric Scintillations on GNSS — A White Paper” by the SBAS-IONO Working Group.

    Manufacturers

    The data presented in Figure 2 was produced by an Ashtech, now Ashtech S.A.S. Z-XII GPS receiver. The data presented in Figure 5 was obtained from Javad, now Javad GNSS and Topcon Legacy GPS receivers and GPS Silicon Valley, now NovAtel GSV4004 GPS scintillation receivers. The data presented in Figure 8 was obtained from a non-commercial receiver.

    The Satellite-Based Augmentation Systems Ionospheric Working Group was formed in 1999 by scientists and engineers involved with the development of the Satellite Based Augmentation Systems in an effort to better understand the effects of the ionosphere on the systems and to identify mitigation strategies. The group now consists of over 40 members worldwide.

    The scintillation white paper was principally developed by Bertram Arbesser-Rastburg, Yannick Béniguel, Charles Carrano, Patricia Doherty, Bakry El-Arini, and Todd Walter with the assistance of other members of the working group.

    FURTHER READING

    • SBAS-IONO Working Group White Papers

    Effect of Ionospheric Scintillations on GNSS – A White Paper by the Satellite-Based Augmentation Systems Ionospheric Working Group, November 2010.

    Ionospheric Research Issues for SBAS – A White Paper by the Satellite-Based Augmentation Systems Ionospheric Working Group, February 2003.

    • Scintillation Spatial and Temporal Variability

    “Morphology of Phase and Intensity Scintillations in the Auroral Oval and Polar Cap” by S. Basu, S. Basu, E. MacKenzie, and H. E. Whitney in Radio Science, Vol. 20, No. 3, May–June 1985, pp. 347–356, doi: 10.1029/RS020i003p00347.

    “Global Morphology of Ionospheric Scintillations” by J. Aarons in Proceedings of the IEEE, Vol. 70, No. 4, April 1982, pp. 360–378, doi: 10.1109/PROC.1982.12314.

    “Equatorial Scintillation – A Review” by S. Basu and S. Basu in Journal of Atmospheric and Terrestrial Physics, Vol. 43, No. 5/6, pp. 473–489, 1981, doi: 10.1016/0021-9169(81)90110-0.

    • Effects of Scintillations on GNSS

    “GNSS and Ionospheric Scintillation: How to Survive the Next Solar Maximum by P. Kintner, Jr., T. Humphreys, and J. Hinks in Inside GNSS, Vol. 4, No. 4, July/August 2009, pp. 22–30.

    “Analysis of Scintillation Recorded During the PRIS Measurement Campaign” by Y. Béniguel, J.-P. Adam, N. Jakowski, T. Noack, V. Wilken, J.-J. Valette, M. Cueto, A. Bourdillon, P. Lassudrie-Duchesne, and B. Arbesser-Rastburg in Radio Science, Vol. 44, RS0A30, 11 pp., 2009, doi:10.1029/2008RS004090.

    “Characteristics of Deep GPS Signal Fading Due to Ionospheric Scintillation for Aviation Receiver Design” by J. Seo, T. Walter, T.-Y. Chiou, and P. Enge in Radio Science, Vol. 44, RS0A16, 2009, doi: 10.1029/2008RS004077.

    “GPS and Ionospheric Scintillations” by P. Kintner, B. Ledvina, and E. de Paula in Space Weather, Vol. 5, S09003, 2007, doi: 10.1029/2006SW000260.

    A Beginner’s Guide to Space Weather and GPS by P. Kintner, Jr., unpublished article, October 31, 2006.

    “Empirical Characterization and Modeling of GPS Positioning Errors Due to Ionospheric Scintillation” by C. Carrano, K. Groves, and J. Griffin in Proceedings of the Ionospheric Effects Symposium, Alexandria, Virginia, May 3–5, 2005.

    “Space Weather Effects of October–November 2003” by P. Doherty, A. Coster, and W. Murtagh in GPS Solutions, Vol. 8, No. 4, pp. 267–271, 2004, doi: 10.1007/s10291-004-0109-3.

    “First Observations of Intense GPS L1 Amplitude Scintillations at Midlatitude” by B. Ledvina, J. Makela, and P. Kintner in Geophysical Research Letters, Vol. 29, No. 14, 1659, 2002, doi: 10.1029/2002GL014770.

    • Previous “Innovation” Articles on Space Weather and GNSS

    “GNSS and the Ionosphere: What’s in Store for the Next Solar Maximum?” by A. Jensen and C. Mitchell in GPS World, Vol. 22, No. 2, February 2011, pp. 40–48.

    “Space Weather: Monitoring the Ionosphere with GPS” by A. Coster, J. Foster, and P. Erickson in GPS World, Vol. 14, No. 5, May 2003, pp. 42–49.

    “GPS, the Ionosphere, and the Solar Maximum” by R.B. Langley in GPS World, Vol. 11, No. 7, July 2000, pp. 44–49.

  • Innovation: GNSS Antennas and Humans

    Innovation: GNSS Antennas and Humans

    A Study of Their Interactions

    By Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle

    GPS World photo
    INNOVATION INSIGHTS by Richard Langley

    GPS IS VIRTUALLY UBIQUITOUS with more than 400 million units estimated to be in use in the United States alone. Some of these units are standalone devices such as those used in surveying and timing applications and those used for vehicle navigation or tracking with permanent or temporary mountings. However, the majority of the units are integrated into cellular telephones, tablet computers, personal digital assistants, watches, cameras, and other devices, which are designed to be operated in close contact with the human body. We even now have GPS shoes!

    It is well known that the performance of the antenna of a radio receiver can be affected when it is used in close proximity to the human body. We only have to touch the whip antenna of a portable AM/FM or scanner radio to convince ourselves of the effect. So, when we use a handheld GPS receiver or wear a GPS watch, or put a GPS-equipped cellular telephone up to our ear, are there any effects on the operation of the receiver?

    It turns out that there are four major effects that can change the performance of a GPS (or other GNSS) receiver antenna when placed near or on the human body. The impedance of the antenna may be changed causing a drop in power transfer to the receiver front end. The center frequency and bandwidth of the antenna may be changed again resulting in a loss of received power. The gain pattern of the antenna may be changed. However, the change may be favorable, improving reception for a given satellite azimuth and elevation angle. And lastly, there will be close-range multipath between the antenna and the body skin.

    All of these factors need to be taken into consideration when a manufacturer is designing a GPS unit to be operated in close proximity to a human body. Trade-offs might be possible and certain designs may make the antenna less likely to interact with its surroundings.

    But how does one go about assessing the antenna’s performance in a repeatable and quantifiable way?

    In this month’s column, a team of researchers from The University of Calgary report on tests conducted on two different types of GPS antennas operated in the vicinity of a human phantom — an artificial body with similar electromagnetic properties as that of a real human.

    “Innovation” features discussions about advances in GPS technology, its applications, and the fundamentals of GPS positioning. The column is coordinated by Richard Langley, Department of Geodesy and Geomatics Engineering, University of New Brunswick. To contact him with topic ideas, email him at lang @ unb.ca.

    GNSS-based navigation is the foundation of many pedestrian navigation systems. The use and benefit of GNSS receivers to locate people has increased dramatically over the past few years. Pedestrian navigation applications include mobile phone users, first responders, health and activity monitoring, consensual tracking (such as offender management), recreational use, and tracking of military personnel. GNSS navigation systems are commonly available in watches and personal entertainment devices. Some applications contain GNSS receivers and antennas in shoes, glasses, and jackets. Since each application using a GNSS receiver to locate people requires an antenna, the optimal type, size, and location on the body is becoming increasingly important.

    This article addresses adverse antenna effects when the antenna is placed near or on the human body, specifically in the reactive near field at the GPS L1 frequency. Using real data collected on a human phantom over prolonged periods, the changes within the antenna are observed as a function of distance from the body. Thus, a performance profile can be generated to quantify the power loss incurred by loading the antenna. The study applies equally well to all GNSS operating at or near the GPS L1 frequency.

    The researchers have theoretically addressed performance of GPS antennas in close proximity to a human body. Using simulations to provide analysis of antenna detuning effects, one research group showed a 24.4-MHz shift in the resonance frequency of the antenna when placed 10–40 millimeters from a simulated human chest. The resonance shift was common at all distances, although the return loss decreased as the antenna was moved further away from the chest.

    A few studies have developed antennas to be located in protective (or otherwise) garments for specific applications. Our team previously analyzed the impact of antenna location on the human body by comparing the solution of eight identical and simultaneous navigation solutions.

    Antenna-Body Interaction

    Antenna detuning refers to the consequence of the electrical interaction between an antenna and an adjacent object, the body of a user in this context, which causes the center frequency of the antenna to deviate from the desired center frequency. More generally, there are several effects that serve to degrade antenna performance that arise when an antenna operates near the body of a user.

    The first of these effects is a change in the impedance of the antenna, as shown in FIGURE 1. (See online sidebar for antenna and electromagnetic radiation term definitions.) The change results in the impedance of the antenna no longer properly matching that of the network that it is expected to drive, therefore causing incomplete power transfer between the antenna element and the subsequent radio-frequency (RF) stages.

    Selected Antenna and Electromagnetic Radiation Terms

    Axial ratio. A measure of the polarization ellipticity of an antenna designed to receive circularly polarized signals. An axial ratio of unity, or 0 dB, implies a perfectly circularly polarized antenna.

    Bandwidth. The range of frequencies over which an antenna is designed to operate efficiently. The bandwidth limits are typically determined by a particular reduction in gain compared to that at the antenna’s center frequency; for example, 3 dB or 10 dB.

    Conductivity. A measure of a material’s ability to conduct an electric current. The reciprocal of resistivity. Units are mhos per meter.

    Dielectric. A material in which there are no free charges that can move through it under the influence of an electric field. An insulator. However, minute displacements of positive and negative charges in opposite directions are possible. A dielectric in which this charge displacement has taken place is said to be polarized.

    Far field. The area sufficiently far from an antenna where the gain pattern is essentially independent of distance. In the far field, the power of an electromagnetic wave traveling in free space drops off as the square of the distance from the transmitting antenna.

    Fresnel reflection coefficient. A measure of the degree of reflection of an electromagnetic wave at the interface between two media. Dependent on the properties of the media, the polarization of the wave, and the angle of incidence.

    Gain. For a transmitting antenna, the ratio of the radiation intensity in a given direction to the radiation that would be obtained if the power accepted by the antenna was radiated isotropically. For a receiving antenna, it is the ratio of the power delivered by the antenna in response to a signal arriving from a given direction compared to that delivered by a hypothetical isotropic reference antenna.

    Gain (amplitude) pattern. The spatial variation of an antenna’s gain.

    Human phantoms. Models of parts of the human body used in engineering, science, and medical studies designed to mimic a particular physical, chemical, or electrical behavior.

    Impedance. The complex ratio of the voltage to the current in an alternating current circuit. Sometimes called complex resistance in which case the absolute value of the complex resistance is called the impedance. Units are ohms.

    Lossy material. A material in which a significant amount of the energy of a propagating electromagnetic wave is absorbed (dissipated) per unit distance traveled by the wave.

    Near field. The region around an antenna within a few wavelengths where there are strong inductive and capacitive effects from the currents and charges in an antenna that cause electromagnetic components not to behave like far-field radiation. Within the radiating part of the near field, the gain pattern is dependent on the distance from the antenna.

    Polarization. The sense of vibration of electromagnetic radiation. There are two main types of polarization: linear, in which the radiating wave’s electric field vector is confined to a particular direction (typically vertical or horizontal); and circular, where the electric field vector rotates as the wave propagates through space. Depending on the sense of rotation, a signal’s waves may be left-hand or, as with GPS signals, right-hand circularly polarized. For maximum response, the polarization of a receiving antenna should match the polarization of the signals.

    (Absolute) Permittivity. A measure of how an electric field affects, and is affected by, a dielectric material. In a sense, it describes a material’s ability to transmit (or “permit”) an electric field. Since the response of most materials to external fields generally depends on the frequency of the field, permittivity is expressed as a complex quantity with real and imaginary components as a function of frequency. Units are farads per meter.

    Relative permittivity. The ratio of the permittivity of a material to that of free space or a vacuum. Also called the dielectric constant. Unitless.

    Return loss. A measure of the effectiveness of power delivery from a transmission line to a load such as an antenna or vice versa. If the power incident on an antenna is Pin and the power reflected back to the source is Pref, the degree of mismatch between the incident and reflected power in the traveling waves is given by the ratio  Pin/Pref.  Units are dB. Functionally related to the Fresnel reflection coefficients and VSWR.

    Voltage standing wave ratio (VSWR). A measure of the size of the reflected waves in a transmission line due to impedance mismatches between the line and a connected antenna. The ratio of the maximum voltage along the line to the minimum voltage along the line. Ideally, an antenna should have a VSWR value of unity.

    FIGURE 1. Change in the reactive portion of the impedance of a patch antenna versus separation distance between the antenna element and imitation human skin (Courtesy, Buckley et al., 2010; see Further Reading). Credit: Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle and Richard Langley
    FIGURE 1. Change in the reactive portion of the impedance of a patch antenna versus separation distance between the antenna element and imitation human skin (Courtesy, Buckley et al., 2010; see Further Reading).

    The figure provides an example of the impedance for a patch antenna plotted against the separation distance of a simulated human wrist. When mounted directly on the user’s skin surface, this specific antenna gains significant reactive impedance that results in a large voltage standing wave ratio (VSWR) with the network.

    A second effect of antenna proximity to human skin is the alteration of the center frequency, as well as the alteration of the antenna bandwidth. Depending on the bandwidth of the signal of interest, the bandwidth of the antenna element, and the degree of center-frequency shifting and bandwidth loss experienced, these factors can contribute to significant loss of received power.

    Thirdly, it is important to note that in some configurations, a “lossy” medium adjacent to an antenna may improve the apparent performance of the antenna due to changes in its gain pattern that result in better receive or transmit performance for a given azimuth and elevation angle.

    For any application in which the antenna may be either in free space or directly adjacent to a lossy medium such as a human body, the use of balanced antennas is recommended. The image current of a balanced antenna is contained within complementary structures of the antenna itself, not within the casing or adjacent material of the antenna, therefore making the antenna much less likely to interact with surrounding media.

    Fourth, the close proximity of a reflective material increases close-range multipath. If the distance between the reflector (that is, skin) and the antenna is close to half a wavelength, giving a 180º phase shift of the carrier, deconstructive interference can occur. There are several factors that contribute to this including the back lobe of the antenna gain pattern, reflection coefficient of the skin beneath the antenna, and the incident angle of the incoming ray. Approximation via simple ray tracing becomes dauntingly complex due to the variation of the antenna properties listed above, resulting from detuning. Therefore, observation of the effect becomes easier than modeling an incoming ray and its multipath counterparts.

    Phantom Body Simulation

    To conduct an assessment of the impact of the human body on the radiation patterns of diverse antennas in the context of tracking GNSS signals, a human body phantom has been designed for collecting the experimental data. Variations of the locations and orientations of the antenna rigidly mounted on a human shoulder, head, or any other locations would render the repeatability and comparison of the collected data hardly feasible. Furthermore, the distance that separates the antenna from the human body surface could only be precisely controlled using an artificial modeling of the human body. Therefore, a human body phantom is required for productive analysis.

    Because the human body is mainly composed of water, the presence of human tissue in the vicinity of the antenna introduces an absorption and reflective effect that alters the performance of the antenna. Different mathematical models have been developed for representing the different component combinations of a human body. Based on the study of numerous women and men of different ages and sizes, a classic model predicting the fat-free mass of a person has been developed and assumes that 73 percent of a human body consists of water. Looking at the elemental composition in the human body, it can be found that a concentration of 7 grams of salt per liter of water provides an acceptable modeling of the human tissues. Complex shapes of the human body are used for modeling more precisely the layered structure of the human tissues using either a more realistic human phantom or a more detailed model comprising the extensive data on the dielectric properties of each layer constituting the human tissues of interest. For context of this study, the phantom was kept simple and was made of a large plastic container filled with a 7 percent concentration of a saline solution.

    The radiative transfer of the human body phantom on the reception of GNSS signals can be evaluated through the understanding of the dielectric permittivity of the solution. Different models, including the Wagner, Debye, Cole & Cole, or Fricke, are commonly used for studying the dielectric behavior of biological tissues. The Debye model gives the permittivity of an aqueous saline solution of salinity, S, at a fixed temperature, t, as

    Inn-Eq1 .Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle (1)

    where

    Screen shot 2013-01-04 at 10.01.10 PM . Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle is the angular frequency (Hz),

    εi equals 8.8419 ×10-12 (farads per meter),

    τ is the relaxation time (seconds),

    σ is the ionic conductivity of the dissolved salts (mhos per meter), and

    ε0 and ε∞ are the static and high frequency dielectric constants.

    Equation (1) gives the dielectric proprieties of the human phantom solution for a specific temperature, saline concentration, and temperature. The experiments we conducted and report on in this article lasted several days and were conducted outside, which unfortunately resulted in temperature fluctuations. Consequently, the 7 percent saline solution over the temperature range of 11º to 31º C for L1 (1575.42 MHz) results in a 9 percent variation of permittivity. As shown in FIGURE 2, the dielectric constant over the experimental temperature range is in the interval [74.6, 81.9]. Because the variation is small, the permittivity value can be closely approximated to a mean value of 78.

    FIGURE 2. Real part of the permittivity of the human body phantom as a function of temperature for the GPS L1 frequency. Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle
    FIGURE 2. Real part of the permittivity of the human body phantom as a function of temperature for the GPS L1 frequency.

    Reflection Coefficient of the Phantom Body

    The Fresnel reflection coefficients for a smooth flat surface depend on frequency, the incident angle, polarization, and ground characteristics. Since the container is full of salted water it can also be considered a reflective surface.

    The relative permittivity of the saline solution given in Equation (1) can be reformatted as

    Inn-Eq2 . Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle (2)

    The reflection coefficients with vertical and horizontal polarizations, respectively, of the electromagnetic wave on the surface of the saline water are given by the following Fresnel equations:

    Inn-Eq3 . Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle(3)

    Inn-Eq4 . Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle(4)

    where Rv and Rh are the vertical and horizontal polarized reflection coefficients, respectively, and θ is the incident angle.

    Assuming that the water surface is flat and infinite, Equations (3) and (4) are plotted against the incident angle in FIGURE 3. The reflection coefficients were estimated using a mean temperature of 21°C, a salt concentration of 7 percent and at the L1 frequency.

    FIGURE 3. Fresnel coefficient for L1 considering a flat surface of salted water. Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle
    FIGURE 3. Fresnel coefficient for L1 considering a flat surface of salted water.

    While the saline solution of the human phantom has an angle of incidence and direction of polarization dependent on reflectivity, the fact that the GPS carrier is circularly polarized must be considered. Due to the circular polarization of the carrier and that of most antenna elements intended for GPS use, the received signal strength of the reflected wave will always appear to be equal to or higher than that of the reflected portion of the horizontal polarization.

    Test Setup

    To evaluate the change in gain pattern as function of distance from the phantom, we collected 24-hour data segments. These segments allowed the receiver to observe all satellites. A high-performance GPS L1 receiver module evaluation kit was used with two antennas. The first was a patch antenna while the second was a quadrifilar helix antenna. FIGURE 4 shows both antennas without their coverings. Each antenna has a built-in low noise amplifier (LNA). The antenna specifications are listed in TABLE 1.

    FIGURE 4. Patch (above) and quadrifilar (below) antennas used in the tests. Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle
    FIGURE 4. Patch (above) and quadrifilar (below) antennas used in the tests.
    TABLE 1. Antenna specifications. Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle
    TABLE 1. Antenna specifications.

    A water container holding the saline solution was placed on the roof of a building as shown in FIGURE 5. The container had a slight inclination to move a small air pocket to the corner of the container away from the antenna. After a successful 24-hour data collection period, the antenna was supported by a small plastic box and oriented in the same direction. Six vertical distances were selected, namely 0, 11, 22, 30, 41, and 52 millimeters.

    FIGURE 5. Data collection with patch antenna fixed to phantom body. Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle
    FIGURE 5. Data collection with patch antenna fixed to phantom body.

    The gain pattern as measured by the C/N0 values of the path antenna is shown in FIGURE 6. In general, the largest effect is seen near the zenith where the power decreased by 10–15 dB when the antenna was 22 millimeters from the phantom body. It is also observed that the effect is maximized at 22 millimeters, and then reverts back to near normal operation at 52 millimeters. Additionally, at lower elevation angles (< 30º), the gain behaves more linearly, where the largest distance has the least gain, while the smallest distance has the most gain. The effect of the phantom body appears to flatten the gain pattern.

    The pattern shown in Figure 6 shows the effect of the proximity to the phantom body over all elevation angles. However, a prominent pattern emerges for measurements made at elevation angles of 45º and 85º. In the case of a 22-millimeter antenna distance from the body, a significant power decrease occurs. For satellites with an 85º elevation angle, nearly 8 dB is lost compared to 5 dB loss at a 45º elevation angle.

    FIGURE 6. Gain pattern of the patch antenna as measured by the measured C/N0 at all elevation angles as a function of antenna distance from body. Elevation angles [0º, 90º] have azimuths [180º, 360º], while elevation angles [90º, 180º] have azimuths [0º, 180º]. Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle
    FIGURE 6. Gain pattern of the patch antenna as measured by the measured C/N0 at all elevation angles as a function of antenna distance from body. Elevation angles [0º, 90º] have azimuths [180º, 360º], while elevation angles [90º, 180º] have azimuths [0º, 180º].
    FIGURE 7 provides the trend as a function of distance from the body. The trend of the power loss at 22 millimeters is common on all measurements, albeit more significant for higher-elevation-angle satellites. For satellite measurements made at an 85º elevation angle, the power varies by 12 dB. When all measurements are considered, which includes more frequent lower-elevation-angle satellite measurements and the fact that the gain pattern deviates significantly at higher elevation angles (as shown in Figure 6), the fluctuation is less prominent.

    FIGURE 7. Mean C/N0 measurements of the patch antenna from all measurements and those only at 45º and 85º elevation angles as a function of antenna distance from the body. Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle
    FIGURE 7. Mean C/N0 measurements of the patch antenna from all measurements and those only at 45º and 85º elevation angles as a function of antenna distance from the body.

    To assess the cause of the impact, we removed the phantom and replaced it with a flat aluminum reflector placed beneath the antenna. The antenna was then placed at the same distances above the reflector as previously. Since the gain pattern had been established and this test was to observe the effect of the reflector, only 60 seconds of data was collected at each distance.

    FIGURE 8 provides the change in C/N0 for two tests, which has a comparable trend to that of Figure 7. From the corroboration of the two tests, it appears that the salt water provides similar multipath effects to that of the aluminum sheet. The power loss is then attributed to destructive interference.

    FIGURE 8. Mean C/N0 measurements (over 60 seconds) of satellite PRN 8 with 85º elevation angle when placed above an aluminum reflector. Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle
    FIGURE 8. Mean C/N0 measurements (over 60 seconds) of satellite PRN 8 with 85º elevation angle when placed above an aluminum reflector.

    Similar data collections were conducted with the quadrifilar helix in order to assess its ability to perform close to the human phantom. The quadrifilar antenna has the LNA circuitry vertically below the antenna and therefore was placed horizontally on the water container. FIGURE 9 shows its gain pattern. The overall C/N0 is lower but is subject to less variation compared to that of the patch antenna. In general, we noticed lower C/N0 values with the quadrifilar antenna, regardless of the environment and despite the LNA having 5 dB more amplification. Some moderate variations of up to 10 dB appear on the east side of the antenna (zenith angle [0º, 90º]), but overall the pattern appears to be more regular.

    FIGURE 9. Gain pattern of the quadrifilar antenna as measured by the C/N0 of all measurements as a function of antenna distance from body. Elevation angles [0º, 90º] have azimuths [180º, 360º], while elevation angles [90º, 180º] have azimuths [0º, 180º]. Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle
    FIGURE 9. Gain pattern of the quadrifilar antenna as measured by the C/N0 of all measurements as a function of antenna distance from body. Elevation angles [0º, 90º] have azimuths [180º, 360º], while elevation angles [90º, 180º] have azimuths [0º, 180º].
    The overall power variation was assessed in a similar method. FIGURE 10 shows cubic-like functions with 3-dB variations. There is also no consistent downward power loss trend at 22 millimeters as observed with the patch antenna. As expected, due to the balanced nature of the quadrifilar antenna, the degree of apparent power loss caused by adjacent material is substantially lower compared to the patch antenna. While the peak level of power received is not as high as that experienced with the patch antenna, the consistency of the received power level is better.

    FIGURE 10. Mean C/N0 measurements of the quadrifilar antenna from all measurements and those only at 45º and 85º elevation angles as a function of antenna distance from the body. Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle
    FIGURE 10. Mean C/N0 measurements of the quadrifilar antenna from all measurements and those only at 45º and 85º elevation angles as a function of antenna distance from the body.

    Conclusions

    We have investigated the impact of the proximity of the human body on received signal power associated with operation of L1 GPS antennas through experimental tests. GPS signals have been collected using two different antenna types (a patch antenna and a quadrifilar helix antenna), placed on a human body phantom with different separation distances. A strong relationship between these distances and the averaged received signal power has been observed for both antennas with overall lower C/N0 values for the quadrifilar antenna. The largest attenuation is not observed when the antenna is directly adjacent to the user body but when it is about 22 millimeters above it. We found that the attenuation mainly results from destructive interference due to multipath. These results suggest that body-mounted GPS antennas should be directly in contact with the user’s body for achieving better tracking performance. Our future research will include theoretically assessing the experimental results for better understanding of the underlying effects.

    Acknowledgments

    This article is based on the paper “GNSS Antenna-Human Body Interaction” presented at ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 19–23, 2011. The authors would like to thank Prof. Ron Johnston, Dept. of Electrical and Computer Engineering, The University of Calgary, for his insight and consultation in preparing that paper. We thank John Buckley, Tyndall National Institute, Ireland, and his co-authors for permission to use Figure 1, a version of which appears in “The Detuning Effects of a Wrist-Worn Antenna and Design of a Custom Antenna Measurement System” (see Further Reading).

    Manufacturers

    The tests discussed in this article used a u-blox AG EVK-6T evaluation kit using a LEA-6T L1 GPS module, an Allis Communication Co. Ltd. M827B active L1 patch antenna, and a Sarantel Ltd. SL1206 active L1 quadrifilar helix antenna.

    Jared B. Bancroft is a senior research engineer in the Position, Location And Navigation (PLAN) Group in the Department of Geomatics Engineering at The University of Calgary in Calgary, Alberta, Canada. He received his Ph.D. in geomatics engineering in 2010 and has worked in the area of navigation since 2004. Dr. Bancroft’s research interests include pedestrian and vehicular navigation through data fusion of sensors and satellite navigation data.

    Valérie Renaudin is a senior research associate in the PLAN Group. She received an M.S. in geomatics engineering from the Ecole Supérieure des Géomètres et Topographes, France, in 1999 and a doctorate in geomatics engineering from the Ecole Polytechnique Fédérale de Lausanne, in 2009. She was previously the technical director at Swissat AG. Her research interests include low-cost sensors, hybridization techniques, magnetometers, and indoor navigation.

    Aiden Morrison is a senior research associate in the PLAN Group. He received his B.Eng. in electrical engineering from Ryerson University, Canada, in 2006 and a Ph.D. in geomatics engineering from The University of Calgary in 2010. His research interests include development of integrated navigation systems.

    Gérard Lachapelle holds a Canada Research Chair in Wireless Location in the Department of Geomatics Engineering at The University of Calgary, where he has been a professor since 1988 and heads the PLAN Group. He has been involved in a multitude of GNSS R&D projects since 1980, ranging from RTK positioning to indoor location and GNSS signal processing enhancements.

    Further Reading

    • Previous Work by Authors
    “GPS Observability and Availability for Various Antenna Locations on the Human Body” by J.B. Bancroft, G. Lachapelle, T. Williams, and J. Garrett in Proceedings of ION GNSS 2010, the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 21–24, 2010, pp. 2941–2951.

    • GNSS Antennas
    “Mobile-Phone GPS Antennas: Can They be Better?” by T. Haddrell, M. Phocas, and N. Ricquier in GPS World, Vol. 21, No. 2, February 2010, pp. 29–35.

    “GNSS Antennas: An Introduction to Bandwidth, Gain Pattern, Polarization, and All That” by G.J.K. Moernaut and D. Orban in GPS World, Vol. 20, No. 2, February 2009, pp. 42–48.

    “A Primer on GPS Antennas” by R.B. Langley in GPS World, Vol. 9, No. 7, July 1998, pp. 73–77.

    • Interaction between Receiving Antennas and Human Body Parts
    “The Detuning Effects of a Wrist-Worn Antenna and Design of a Custom Antenna Measurement System” by J. Buckley, K.G. McCarthy, B. O’Flynn, and C. O’Mathuna in Proceedings of the 40th European Microwave Conference, Paris, France, 28–30 September 2010, pp. 1738-1741.

    “One-Layer GPS Antennas Perform Well Near a Human Body” by T. Kellomaki, J. Heikkinen, and M. Kivikoski in Proceedings of EuCAP 2007, the Second European Conference on Antennas and Propagation, Edinburgh, Scotland, November 11–16, 2007, 6 pp.

    “Effects of Human Body Interference on the Performance of a GPS Antenna” by M. Ur Rehman, Y. Gao, X. Chen, C.G. Parini, and Z. Ying in Proceedings of EuCAP 2007, the Second European Conference on Antennas and Propagation, Edinburgh, Scotland, November 11–16, 2007, 4 pp.

    • Wearable Antennas
    “Design of a Protective Garment GPS Antenna” by L. Vallozzi, W. Vadendriessche, H. Rogier, C. Hertleer, and M.L. Scarpello in Microwave and Optical Technology Letters, Vol. 51, No. 6, June 2009, pp. 1504–1508, doi: 10.1002/mop.24372.

    “Wearable Antennas in the Vicinity of Human Body” by P. Salonen, Y. Rahmat-Samii, and M. Kivikoski in Proceedings of the IEEE Antennas and Propagation Society International Symposium, Monterey, California, June 20–26, 2004, pp. 467–470, doi: 10.1109/APS.2004.1329675.

    “A Small Planar Inverted-F Antenna for Wearable Applications” by P. Salonen, L. Sydänheimo, M. Keskilammi, and M. Kivikoski in Digest of Papers, the Third International Symposium on Wearable Computers, San Francisco, California, October 18–19, 1999, pp. 95–100, doi: 10.1109/ISWC.1999.806679.

    • Dielectric Properties of Human Tissue and Sea Water
    “New Permittivity Measurements of Seawater” by W. Ellison, A. Balana, G. Delbos, K. Lamkaouchi, L. Ey, C. Guillou, and C. Prigent in Radio Science, Vol. 33, No. 3, 1998, pp. 639–648, doi: 10.1029/97RS02223.

    Compilation of the Dielectric Properties of Body Tissues at RF and Microwave Frequencies by C. Gabriel, Final Technical Report, AL/OE-TR-1996-0004, Radio Frequency Radiation Division, Occupational and Environmental Health Directorate, Brooks Air Force Base, Texas, January 1996.

    “Studies on Body Composition. III. The Body Water and Chemically Combined Nitrogen Content in Relation to Fat Content” by N. Pacen and E.N. Rathurn in Journal of Biological Chemistry, Vol. 158, 1945, pp. 685–691.

    • Human Phantoms
    “Solid Phantoms for Evaluation of Interactions Between the Human Body and Antennas” by K. Ito and H. Kawai in Proceedings of IWAT 2005, the 2005 IEEE International Workshop on Antenna Technology: Small Antennas and Novel Metamaterials, Singapore, March 7–9, 2005, pp. 41–44, doi: 10.1109/IWAT.2005.1460993.

    “A High-Precision Real Human Phantom for EM Evaluation of Handheld Terminals in a Talk Situation” by K. Ogawa, T. Matsuyoshi, H. Iwai, and N. Hatakenaka in 2001 Digest, IEEE Antennas and Propagation Society International Symposium, Boston, Massachusetts, July 8–13, 2001, Vol. 2, pp. 68–71, doi: 10.1109/APS.2001.959623.

  • Innovation: Know Your Enemy

    Innovation: Know Your Enemy

    Signal Characteristics of Civil GPS Jammers

    By Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys

    GPS jamming is a continuing threat. A detailed understanding of how the available jammers work is necessary to judge their effectiveness and limitations. A team of researchers from Cornell University and the University of Texas at Austin reports on their analyses of the signal properties of 18 commercially available GPS jammers.

    GPS World photo
    INNOVATION INSIGHTS by Richard Langley

    GPS IS AT WAR. It is a major asset for United States and allied military forces in a number of operating theaters around the world in both declared and undeclared conflicts. But GPS is at war on the domestic front, too — at war against a proliferation of jamming equipment being marketed to cause deliberate interference to GPS signals to prevent GPS receivers from computing positions to be locally stored or relayed via tracking networks.

    There have been many notable examples of deliberate jamming of GPS receivers. Many more likely go undetected each day. In 2009, outages of a Federal Aviation Administration reference receiver at Newark Liberty International Airport close to the New Jersey Turnpike were traced to a $33, 200 milliwatt GPS jammer in a truck that passed the airport each day. The driver was reportedly arrested and charged. In July 2010, two truck thieves in Britain were jailed for 16 years. They used GPS jammers to prevent the trucks from being tracked after the thefts. And in Germany, some truck drivers have been using jammers to evade the country’s GPS-based road-toll system.

    The U.S. and some foreign governments have enacted laws to prohibit the importation, marketing, sale or operation of these so-called personal privacy devices. Nevertheless, a certain number of jammers are in the hands of individuals around the world and they continue to be available from manufacturers and suppliers in certain countries. So, GPS jamming is a continuing threat both at home and abroad and a detailed understanding of how the available jammers work is necessary to judge their effectiveness and limitations. This information will also help in developing countermeasures that could be incorporated into GPS receivers to limit the impact of jammers.

    Jammers constitute an enemy force, and as the Chinese General Sun Tzu stated in the Art of War more than 2,000 years ago, battles will be won by knowing your enemy. In the last verse of Chapter Three, he states:

    So it is said that if you know your enemies and know yourself, you can win a hundred battles without a single loss.

    If you only know yourself, but not your opponent, you may win or may lose.

    If you know neither yourself nor your enemy, you will always endanger yourself.

    In this month’s column, a team of researchers from Cornell University and the University of Texas at Austin reports on their analyses of the signal properties of 18 commercially available GPS jammers. The enemy has been exposed.

    The Global Positioning System has become increasingly incorporated into civilian infrastructure. The increase in GPS-integrated systems has caused a proportional increase in the vulnerability of these systems to jamming and interference. The interests of individuals or groups willing to break the law may be served by interfering with the normal operation of GPS-enabled systems. As a result, in recent years many GPS jamming devices have become available for purchase over the Internet. These relatively cheap devices, some costing less than an inexpensive GPS receiver, pose a significant risk to the normal operation of many systems reliant on GPS.

    Many types of intentional radio frequency (RF) interference exist, including tones, swept waveforms, pulses, narrowband noise, and broadband noise. There are a number of methods for mitigating the effects of jamming and interference, and additional methods exist to locate the sources of the interference. Mitigation and location methods can be improved by use of a priori information about the interference source. This article provides such a priori information for a set of jammers and assesses their threats. Its results are based on two tests. The first test records raw RF data from a selection of jammers and analyzes it using fast Fourier transform (FFT) spectral methods. The second test evaluates the effective range of a subset of the GPS jammers using a commercial off-the-shelf (COTS) receiver.

    The article presents results based on 18 civil GPS jammers. There are other types of GPS jammers for sale that were not tested. Furthermore, civil jammer behavior and design is likely to evolve over time. In this article, we draw conclusions based on only the jammers that we tested.

    Overview of Civil GPS Jammers

    Devices that claim to jam or “block” GPS signals are widely available through a number of websites and online entities. The cost of these devices ranges from a few tens of dollars to several hundred. Their price does not seem to correlate with the claims made by the purveyors of these devices regarding the features and effectiveness of the product in question. Effective ranges from a few meters to several tens of meters are advertised, but the actual effective ranges are significantly greater. Claimed and true power consumptions range from a fraction of a watt to several watts.

    We grouped the GPS jammers we examined in this article into three categories based on morphology. The first is a group of jammers designed to plug into an automotive 12-volt auxiliary power supply outlet (cigarette lighter socket); this class of jammer is referred to in the remainder of this article as Group 1. The second category contains those jammers that are both powered by an internal rechargeable battery and that have an external antenna connected via an SMA connector; these jammers are referred to as Group 2. The jammers in Group 3 are disguised as cell phones; they have batteries but no external antennas. Figure 1 shows an example of a device from each of Groups 1–3.

    Credit: Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys
    Figure 1. Three jammers are depicted, from left to right Jammers 1, 5, and 15 from Groups 1, 2, and 3, respectively.

    All 18 jammers broadcast power at or near the L1 carrier frequency, six broadcast power at or near the L2 carrier frequency, and none broadcast power at or near the L5 carrier frequency. Some of the jammers also broadcast power at frequencies outside of the GPS bands, typically cellular phone or Wi-Fi bands, but those frequencies are outside the scope of this article. Results in this article are for the current power levels broadcast in the GPS L1 and L2 bands, but examination of power levels in non-GPS bands indicate that many of these devices could be easily modified to broadcast much more power in the GPS bands.

    The jammer antennas have been removed in most of the testing for this article, but their use in a real-world scenario will modify the jammer behavior. The antennas used by Group 1 and Group 2 jammers are loaded monopole antennas, while those used by the Group 3 jammers are electrically short helical antennas that have approximately the same gain pattern as the loaded monopoles. These antennas broadcast linearly polarized radiation, as opposed to the right-hand circular polarization of GPS signals. The polarization mismatch will cause some loss in received power at a right-hand circularly polarized GPS receiver antenna.

    Jammer Signal Characteristics Test

    The goal of the first set of tests was to record complex samples of the jamming signals and to derive the jammer characteristics from these data. A two-step procedure was used to collect useful data. The first step used a spectrum analyzer to find the frequency range of the jamming signal near L1 and L2. The second step used this frequency information to set the center frequency of a general-purpose RF digitization and signal storage device with a 12-drive RAID storage array. Offline analyses were then conducted on the recorded data.

    The test procedure was as follows. For the first two groups, the jammer was placed inside an RF-shielded test enclosure shown in Figure 2, to prevent any signal leakage, and its SMA signal output port was connected to the relevant data collection device using a shielded coaxial cable. The signal had to pass from the inside to the outside of the RF enclosure using the built-in coaxial feed-through. Note, therefore, that no jammer signal radiation occurred for Group 1 and 2 jammers even inside the RF enclosure. The enclosure was used primarily as a precaution.

    Figure 2. RF-shielded test enclosure. Jammers were operated inside the enclosure to prevent emission of their RF signals. Credit: Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys
    Figure 2. RF-shielded test enclosure. Jammers were operated inside the enclosure to prevent emission of their RF signals.

    None of the Group 3 jammers had external antennas. Therefore, they were allowed to radiate in the RF enclosure using their internal antennas. To capture the signal, a receiving patch antenna with active amplification was placed in the RF enclosure, and the antenna output was connected to the relevant RF recording device via the enclosure’s coaxial feed-through. The jammer and receiving antenna were separated by about 14 centimeters. The patch antenna field-of-view center was pointed directly at the jammer. The jammer was oriented such that the axis of its helical antenna was pointing perpendicular to the line from the receiving antenna to the jammer.

    Jammer Signal Characteristics Test Results

    Although 18 jammers were tested, only a representative subset is discussed here. The signals were analyzed using FFT spectral methods and measurements of in-band power. Figure 3 displays the results of this analysis for a typical jammer from Group 1.

    The top plot of Figure 3 graphs frequency on the vertical scale versus time on the horizontal scale. The bottom plot graphs power on the vertical scale versus time on the horizontal scale. Each vertical slice of the recorded RF data plot is a single FFT frequency spectrum. It covers 62.5 MHz centered on the L1 band and has a resolution of approximately 1 MHz. The relative power spectral density of each slice is indicated by color. The time axes of both plots span 80 microseconds.

    Figure 3. Jammer 4 power spectral density versus time, with color indicating relative power (top plot) and power versus time in a 62.5-MHz band centered at the L1 carrier frequency (bottom plot). Credit: Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys
    Figure 3. Jammer 4 power spectral density versus time, with color indicating relative power (top plot) and power versus time in a 62.5-MHz band centered at the L1 carrier frequency (bottom plot).

    The upper plot of Figure 3 is clearly that of a linear frequency modulation interspersed with rapid resets — a series of linear chirps. Each sweep takes nine microseconds and spans a range of about 14 MHz. This range includes the civil L1 GPS band. The center frequency is depicted by the horizontal red line in the top plot. The power is about 20 milliwatts and remains fairly constant over the sweep.

    Three of the Group 1 jammers appeared to be of the same model and one was slightly different. All of them broadcast power only at L1. Despite their similarities in external appearance, the three jammers of the same model exhibited markedly different signal properties. These differences will be presented later in terms of tabulated frequency modulation characteristics and in-band power levels.

    One of the Group 2 jammers was unusual in two respects, as illustrated in Figure 4. This figure plots the L2 spectrum whose center is indicated by the horizontal red line in the top plot. The first obvious difference from Figure 3 is that the frequency modulation in time is a triangular wave instead of a sawtooth. Additionally, the modulation frequency is very high in comparison to all the other jammers; its period is only about 1 microsecond. Note that the horizontal scale of this figure spans only 8 microseconds, that is, 10 times less than in Figure 3.

    The other Group 2 jammers tended to broadcast sawtooth frequency modulations as in Figure 3. They all broadcast jamming power at L1. Of course, the jammer depicted in Figure 4 broadcast power at L2 as well. Only one other Group 2 jammer had L2 jamming capability. Two of the jammers suffered from poor design of their L1 frequency modulation schemes: they placed no jamming power closer than 4.6 MHz away from the nominal L1 carrier frequency.

    Figure 4. Jammer 10 power spectral density versus time (top plot), with resolution of about 3 MHz and color indicating relative power, and power versus time (bottom plot) in a 62.5-MHz band centered at the L2 carrier frequency. Credit: Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys
    Figure 4. Jammer 10 power spectral density versus time (top plot), with resolution of about 3 MHz and color indicating relative power, and power versus time (bottom plot) in a 62.5-MHz band centered at the L2 carrier frequency.

    Another unusual frequency modulation was encountered in a Group 3 jammer. The L1 results for this jammer are depicted in Figure 5. It seems to show a linear-type frequency modulation distorted by sudden frequency jumps, as seen in the upper plot of the figure. Despite its irregular nature, this waveform maintains its jamming efficacy.

    Figure 5. Jammer 15 power spectral density versus time, with color indicating relative power (top plot) and power versus time in a 62.5-MHz band centered at the L1 carrier frequency (bottom plot). Note the additional frequency jumps in the sweep pattern. Credit: Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys
    Figure 5. Jammer 15 power spectral density versus time, with color indicating relative power (top plot) and power versus time in a 62.5-MHz band centered at the L1 carrier frequency (bottom plot). Note the additional frequency jumps in the sweep pattern.

    All four jammers in Group 3 broadcast power at L1, L2, and additional frequency bands. Three of the jammers appeared to be of the same model, while a fourth was different. Jammers in this group normally use a standard sawtooth frequency modulation. Figure 5 represents the exception.

    Additional types of distortion from the nominal sawtooth frequency modulation have been observed in some of the jammers. Discussion of each additional variation has been omitted here for the sake of brevity. See the authors’ companion conference paper, listed in the Further Reading sidebar for more details.

    Frequency Modulation Periods and Ranges. The frequency modulation characteristics of all 18 jammers are listed in Table 1. The first two columns identify each jammer by group number and jammer number. The sweep period and frequency range for the L1 sweep are shown in the third and fourth columns. The two numbers in the fourth column are the upper and lower bounds of the jamming tone sweep range in megahertz above and below the L1 carrier frequency. For instance, the period between resets of the linear frequency modulation of Jammer 1 is 26 microseconds and the tone sweeps from 25.4 MHz below L1 to 31.3 MHz above L1. The fifth and sixth columns are analogous to the third and fourth columns, but for jamming in the L2 band, with entries only for those jammers that broadcast in this band.

    The sweep periods were calculated using four contiguous sweeps from near the beginning of each data set and another four sweeps 30 seconds later. The sweep periods exhibited standard deviations of less than 1 microsecond. The reported sweep ranges are the minimum and maximum frequency observed in the same data used to calculate sweep periods. The sweep ranges changed by as much as 2.5 MHz between sweeps.

    One can make a number of observations based on Table 1. First, as mentioned previously, jammers which appeared to be of the same model exhibited significant variations in sweep behavior. For instance, Jammers 1, 3, and 4 appeared to be of the same models, yet Jammer 1 has a sweep period nearly three times as long as Jammers 3 and 4. It also has a sweep range four times as wide. Second, some individual jammers were exceptional. For example, Jammer 10 has a sweep period nearly 10 times shorter than any other jammer, and its L1 sweep range exceeded the 62.5 MHz bandwidth recorded by the RF sampling equipment. The sweep range of Jammer 16 also exceeded the sampled bandwidth, though its sweep period was not exceptional. Jammers 12 and 13 do not sweep through the L1 carrier frequency, as indicated by the negative signs in the fourth column of Table 1. Jammer 17 suffered from the same problem, but for both L1 and L2.

    Table 2. Jammer power levels in frequency bands of interest. Credit: Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys
    Table 1. Frequency characteristics of GPS jammers.

    In-Band Jammer Power Levels. The GPS signal is spread over several megahertz by the pseudorandom noise (PRN) codes that modulate the L1 or L2 carrier waves. Different GPS receivers exploit this spreading by processing more or less of the full bandwidth. The RF power of the GPS jamming signal within different bands centered at L1 is an important concern because different receiver RF front-end bandwidths may allow different total amounts of jammer power to pass through them. For example, a C/A-code receiver with a 2-MHz RF front-end bandwidth will pass 10 dB less jammer power than will a 20-MHz bandwidth RF front end of a P(Y)-code receiver if the jammer in question spreads its power evenly over the 20-MHz band centered at the L1 carrier frequency. If the jammer power is concentrated in a 2-MHz range, however, then both receiver front ends will pass equal total jammer power.

    To determine the power in different bandwidths, the raw data were filtered to pass only the bandwidths of interest. The data were digitally filtered using a finite input response (FIR) equiripple band-pass filter, providing 60 dB of attenuation at 2 MHz past the roll-off frequency. Note that a real GPS receiver will probably not have analog filter frequency roll offs as sharp as those used in our work.

    Table 2 presents the results of this study. It reports power measurements averaged over 15 milliseconds in three different bandwidths: 2, 20, and 50 MHz, all centered at the nominal L1 or L2 carrier frequency. The table also indicates whether each jammer broadcasts power at frequencies other than the GPS frequencies. No power data is given for the non-GPS frequencies because they are not the focus of this article.

    A number of observations can be drawn from Table 2. First, there is a large variation in broadcast power among jammers, with Group 2 jammers being on average more powerful. Specifically, Jammer 11 is the most powerful, broadcasting more than a watt in the GPS bands! Second, jammers of the same model broadcast roughly the same amount of power despite the differences in sweep behavior mentioned above. For instance, Jammers 1, 3, and 4 broadcast roughly the same amount of power, and Jammers 15, 17, and 18 do so as well. Third, the poor frequency plans of Jammers 12, 13, and 17 are apparent in the power measurements. These jammers did not sweep a tone through L1 or L2, and effectively no power was measured in the 2-MHz band centered on the L1 or L2 carrier frequencies.

    Table 2. Jammer power levels in frequency bands of interest. Credit: Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys
    Table 2. Jammer power levels in frequency bands of interest.

    Although not shown in the tables, Jammers 12, 13, and 14 exhibited periodic variations in broadcast power. Their peak-to-peak power varies as a sawtooth wave with period approximately 15 milliseconds and amplitude on the order of 10 percent of the total broadcast power.

    The measured power values in Table 2 for jammers of Groups 1 and 2 were derived using direct cable connections. Thus, they report the total power into the transmitting antenna. The power received at a GPS receiver’s RF front end will be affected by any antenna inefficiency, the antenna gain pattern, and the space loss, among other effects.

    In contrast, the power reported for Group 3 jammers includes all of those effects for the given test configuration. Specifically, the receiving antenna picked up only a fraction of the radiated power because the receiving antenna subtended only a fraction of the 4π steradians around the transmitting antenna. Also, the power that was received was boosted by the receiving antenna’s active low-noise amplifier. Finally, the radiation environment inside the RF enclosure is uncertain, and the enclosure constrains the separation of the antennas to be on the order of one wavelength, thereby giving rise to near-field effects. Therefore, the indicated power levels for the Group 3 jammers do not constitute measures of absolute power. The tabulated power levels for Group 3 jammers are included primarily for purposes of comparison within the group.

    Maximum Effective Range Test

    The goal of the second set of tests was to determine the effective ranges of the GPS jammers when interfering with a COTS receiver. A constraint on this test was that it could not broadcast harmful radiation to the environment. Ideally, the jammers and a receiver would be taken outside and tested with all antennas attached. However, this type of test would possibly interfere with other equipment and is illegal in the United States. A close approximation to this scenario can be constructed using a high-fidelity simulated GPS signal, a commercial GPS receiver, a GPS jammer in an RF enclosure, and a set of attenuators to simulate various distances. The setup for the second test is shown in the block diagram of Figure 6.

    I-6 . Credit: Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys
    Figure 6. Block diagram of the test procedure and equipment used to determine the GPS jammers’ effective ranges.

    Each range test involved running a GPS jammer inside the RF enclosure, passing its signal through the enclosure’s coaxial feed-through, and electrically combining that signal with a GPS simulator signal. The combined signal was then input to the antenna connector of the COTS GPS receiver. Attenuators were inserted in-line with the GPS jammer before it arrived at the combiner. Using this setup, two tests were conducted. The first test determined the jamming signal attenuation level necessary for continuous tacking. The second test determined the attenuation level necessary to allow the receiver to acquire the simulator signal within five minutes from a cold start. As will be shown in the next section, the resulting attenuation values can be converted into effective ranges of the jammers if one makes certain reasonable assumptions about transmitting and receiving antenna gains and path losses.

    The simulator power level was set so that the power into the receiver matched that which it would receive from the actual GPS constellation through a typical roof-mounted passive patch antenna. This power level was checked by comparing the resulting C/N0 for all of the visible satellites when using the simulator against typical C/N0 values when using the roof-mounted antenna. Typical levels reported by the receiver were C/N0 = 43 dB-Hz.

    Maximum Effective Range Results

    The jamming signal attenuation levels resulting from the two tests are presented in Table 3. These tests were conducted on one jammer from Group 1 and three jammers from Group 2. No jammers from Group 3 were included because of the broadcast power uncertainties discussed in connection with Table 2.

    The attenuation values by themselves are not very useful, but they can be converted into distance measurements with a number of assumptions. The ratio of received power to transmitted power can be expressed as

    Screen shot 2013-01-05 at 8.55.31 PM . Credit: Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys

    where Gt is the transmitting antenna gain, Gr is the receiving antenna gain, and the term (λ/(4πr))2 is the path loss for radiation of wavelength λ over the distance r. This equation can be solved for the range, r:

    Screen shot 2013-01-05 at 8.55.37 PM . Credit: Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys
    The quantity in this formula that equates to the total electrical jammer attenuation produced in each bench-top test is the product of the antenna gains and the ratio of transmitted to received power: Gt Gr(Pt ⁄Pr ).

    To convert the results in Table 3 into effective ranges, the transmitting and receiving antennas can be assumed to be perfect, lossless, isotropic radiators. In this case, the gain terms, Gt and Gr , are unity. Each measured attenuation value can be converted to the unitless ratio, Pt ⁄Pr , and substituted into the equation for r. Use of this equation at the L1 carrier frequency yields the ranges in Table 4. If the range between the jammer and receiver is less than that listed in the third column of the table, then the jammer will prevent the receiver from tracking and acquiring. If the range is less than that listed in the last column but more than that listed in the third column, the receiver will continue to track but be unable to acquire. The effective ranges are at least an order of magnitude greater than the claims of the jammers’ purveyors.

    TABLE 3 Jammer attenuation levels needed to allow COTS GPS receiver acquisition and tracking. Credit: Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys
    Table 3. Jammer attenuation levels needed to allow COTS GPS receiver acquisition and tracking.
    Screen shot 2013-01-05 at 8.48.59 PM . Credit: Ryan H. Mitch, Ryan C. Dougherty, Mark L. Psiaki, Steven P. Powell, Brady W. O’Hanlon, Jahshan A. Bhatti, and Todd E. Humphreys
    Table 4. Ranges of jammer effectiveness against COTS GPS receiver when using lossless isotropic antennas.

    Distinct scenarios with different antennas can be approximately tested using Table 3 and the range equation. For example, a patch antenna that is oriented perfectly skyward might have 10 dB of attenuation at very low elevation angles, and the jammer might have an additional 3 dB loss due to polarization mismatch. In this scenario, the effective jamming range would be factored down by 10-13/20 = 0.22. In this case, Jammer 11’s tracking interference range would be reduced from 6.1 kilometers to 1.4 kilometers. Additional jammer signal attenuation might occur if the emissions passed through the reduced RF aperture of a vehicle’s body and windows. Such an effect could be incorporated into the range equation to determine a revised effective range.

    Due to the ignored losses in the real system, it would likely be safe to assume that the effective ranges of the GPS jammers would be no greater than those listed in Table 4. The ranges could potentially be greater if a high-gain receiving antenna were aimed directly at the jamming source, or if the jamming source used a high-gain transmitting antenna aimed at the receiver. None of the jammers tested employed such an antenna.

    Summary and Conclusions

    This article has presented the signal properties of 18 commercially available GPS jammers as determined from two types of live experimental tests. The first test examined the frequency structures and power levels of the jammer signals. It showed that all of the jammers used some sort of swept tone method to generate broadband interference. The majority of the jammers used linear chirp signals, all jammed L1, only six jammed L2, and none jammed L5. The sweep period of the jammers is about 9 microseconds on average, and they tend to sweep a range of less than 20 MHz. Some of the jammers’ sweep ranges failed to encompass the target L1 or L2 carrier frequencies.

    The second test provided an estimate of four of the jammers’ effective ranges when deployed against a typical commercial receiver. An upper bound on the effective ranges was calculated for idealized, lossless, isotropic radiating and receiving antennas with matched polarizations. The weakest of the four jammers affected tracking at a range of about 300 meters and acquisition at about 600 meters, while the strongest affected tracking at a range of about 6 kilometers and acquisition at about 8.5 kilometers.

    Acknowledgments

    The authors thank the U.S. Department of Homeland Security for providing interference devices for testing. This article is based on the paper “Signal Characteristics of Civil GPS Jammers” presented at ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 19–23, 2011, where it received a best-presentation-in-session award.

    Manufacturers

    The tests discussed in this article used an Agilent Technologies (www.home.agilent.com) model N1996A spectrum analyzer, a National Instruments PXI-5663 RF vector signal analyzer, a Ramsey Electronics model STE3000B RF shielded test enclosure, an Antcom (www.antcom.com) model 53G1215A-XT-1 patch antenna, and a NovAtel ProPakII-RT2 GPS receiver.

    Ryan H. Mitch is a graduate student in the Sibley School of Mechanical and Aerospace Engineering at Cornell University, Ithaca, New York. He received his B.S. degree in mechanical engineering from the University of Pittsburgh.

    Ryan C. Dougherty is a graduate student in the Sibley School. He holds a B.S. degree in aerospace engineering from the University of Southern California.

    Mark L. Psiaki is a professor in the Sibley School. He received a B.A. degree in physics and M.A. and Ph.D. degrees in mechanical and aerospace engineering from Princeton University.

    Steven P. Powell is a senior engineer with the GPS and Ionospheric Studies Research Group in the Department of Electrical and Computer Engineering at Cornell University. He has M.S. and B.S. degrees in electrical engineering from Cornell University.

    Brady W. O’Hanlon is a graduate student in the School of Electrical and Computer Engineering at Cornell University. He received a B.S. degree in electrical and computer engineering from Cornell University.

    Jahshan A. Bhatti is pursuing a Ph.D. degree in the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas (UT) at Austin, where he also received his M.S. and B.S. degrees. He is a member of the UT Radionavigation Laboratory.

    Todd E. Humphreys is an assistant professor in the Department of Aerospace Engineering and Engineering Mechanics at UT Austin and Director of the UT Radionavigation Laboratory. He received B.S. and M.S. degrees in electrical and computer engineering from Utah State University and a Ph.D. degree in aerospace engineering from Cornell University.

    Further Reading

    • Authors’ Conference Paper

    “Signal Characteristics of Civil GPS Jammers” by R.H. Mitch, R.C. Dougherty, M.L. Psiaki, S.P. Powell, B.W. O’Hanlon, J.A. Bhatti, and T.E. Humphreys in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of The Satellite Division of the Institute of Navigation, Portland, Oregon, September 19–23, 2011, pp. 1907–1919.

    • Vulnerability of GPS

    Vulnerability Assessment of the Transportation Infrastructure Relying on the Global Positioning System – Final Report. John A. Volpe National Transportation Systems Center, Cambridge, Massachusetts, August 29, 2001.

    • GPS Jamming

    “Car Jammers: Interference Analysis” by R. Bauernfeind, T. Kraus, D. Dötterböck, B. Eissfeller, E. Löhnert, and E. Wittmann in GPS World, Vol. 22, No. 10, October 2011, pp. 28–35.

    “GPS Jamming: No Jam Tomorrow” in The Economist, Technology Quarterly Special Section, Vol. 398, Issue 8724, March 12, 2011, pp. 20–21.

    Modern Communications Jamming Principles and Techniques, 2nd ed., by R.A. Poisel, published by Artech House, Boston, Massachusetts, 2011.

    “Jamming GPS: Susceptibility of Some Civil GPS Receivers” by B. Forssell and R.B. Olsen in GPS World, Vol. 14, No. 1, January 2003, pp. 54–58.

    “A Growing Concern: Radiofrequency Interference and GPS” by F. Butsch in GPS World, Vol. 13, No. 10, October 2002, pp. 40–50.

    “Interference Effects and Mitigation Techniques” by J.J. Spilker Jr. and F.D. Natali, Chapter 20 in Global Positioning System: Theory and Applications, Volume I, published by the American Institute of Aeronautics and Astronautics, Inc., Washington, D.C., 1996, pp. 717–771.

    • Government Regulations and Actions Against Jammers

    “Twenty Online Retailers of Illegal Jamming Devices Targeted in Omnibus Enforcement Action,” a Federal Communications Commission press release issued October 5, 2011.

    “FCC Enforcement Bureau Steps up Education and Enforcement,” a Federal Communications Commission press release issued February 9, 2011.

    “Cell Jammers, GPS Jammers, and Other Jamming Devices,” Federal Communications Commission Enforcement Advisory No. 2011-04 issued February 9, 2011, for consumers.

    “Cell Jammers, GPS Jammers, and Other Jamming Devices,” Federal Communications Commission Enforcement Advisory No. 2011-03 issued February 9, 2011, for retailers.

    • Jamming Counter Measures

    “Receiver Certification: Making the GNSS Environment Hostile to Jammers and Spoofers” by L. Scott. Presented to the National Space-Based Positioning, Navigation, and Timing (PNT) Advisory Board, 9th Meeting, November 9–10, 2011, Alexandria, Virginia.

    “The Civilian Battlefield: Protecting GNSS Receivers from Interference and Jamming” by M. Jones in Inside GNSS, Vol. 6, No. 2, March/April 2011, pp. 40–49.

    “Interference Heads-up: Receiver Techniques for Detecting and Characterizing RFI” by P.W. Ward in GPS World, Vol. 19, No. 6, June 2008, pp. 64–73.

    “Jamming Protection of GPS Receivers, Part I: Receiver Enhancements” by S. Rounds in GPS World, Vol. 15, No. 1, January 2004, pp. 54–59.

    “Jamming Protection of GPS Receivers, Part II: Antenna Enhancements” by S. Rounds in GPS World, Vol. 15, No. 2, February 2004, pp. 38–45.

    “Antijamming and GPS for Critical Military Applications,” by A. Abbott in Crosslink, Vol. 3, No. 2, Summer 2003, pp. 36–41.

  • Innovation: Digging into GPS Integrity

    Innovation: Digging into GPS Integrity

    Charting the Evolution of Signal-in-Space Performance by Data Mining 400,000,000 Navigation Messages

    By Liang Heng, Grace Xingxin Gao, Todd Walter, and Per Enge

    There are four important requirements of any navigation system: accuracy, availability, continuity, and integrity. In this month’s column we take a look at one particular aspect of GPS integrity: that of the signal in space and find out how trustworthy is the satellite ephemeris and clock information in the broadcast navigation message.

    GPS World photo
    INNOVATION INSIGHTS by Richard Langley

    BUT THE GREATEST OF THESE IS INTEGRITY. There are four important requirements of any navigation system: accuracy, availability, continuity, and integrity.

    Perhaps the most obvious navigation system requirement, accuracy describes how well a measured value agrees with a reference value, typically the true value. In the case of GPS, we might talk about the accuracy of a range measurement. A receiver actually measures a pseudorange — a biased and noisy measure of the geometric range between the receiver and the satellite. After correcting for satellite ephemeris and satellite clock errors (the primary so-called signal-in-space errors), receiver clock errors, and atmospheric effects, we can get an estimate of the geometric range. How well we account for these errors or biases, will determine the accuracy of the corrected pseudorange measurement and ultimately, the accuracy of a derived position.

    A navigation system’s availability refers to its ability to provide the required function and performance within the specified coverage area at the start of an intended operation. In many cases, system availability implies signal availability, which is expressed as the percentage of time that the system’s transmitted signals are accessible for use. In addition to transmitter capability, environmental factors such as signal attenuation or blockage or the presence of interfering signals might affect availability.

    Ideally, any navigation system should be continuously available to users. But, because of scheduled maintenance or unpredictable outages, a particular system may be unavailable at a certain time. Continuity, accordingly, is the ability of a navigation system to function without interruption during an intended period of operation. More specifically, it indicates the probability that the system will maintain its specified performance level for the duration of an operation, presuming system availability at the beginning of that process.

    The integrity of a navigation system refers to its trustworthiness. A system might be available at the start of an operation, and we might predict its continuity at an advertised accuracy during the operation.

    But what if something unexpectedly goes wrong? If some system anomaly results in unacceptable navigation accuracy, the system should detect this and warn the user. Integrity characterizes a navigation system’s ability to provide this timely warning when it fails to meet its stated accuracy. If it does not, we have an integrity failure and the possibility of conveying hazardously misleading information. GPS has built into it various checks and balances to ensure a fairly high level of integrity. However, GPS integrity failures have occasionally occurred.

    In this month’s column we take a look at one particular aspect of GPS integrity: that of the signal in space and find out how trustworthy is the satellite ephemeris and clock information in the broadcast navigation message.

    The Navstar Global Positioning System is so far the most widely used space-based positioning, navigation, and timing system. GPS works on the principle of trilateration, in which the measured distances from a user receiver to at least four GPS satellites in view, as well as the position and clock data for these satellites, are the prerequisites for the user receiver to fix its exact position. For most GPS Standard Positioning Service (SPS) users, real-time satellite positions and clocks are derived from ephemeris parameters and clock correction terms in navigation messages broadcast by GPS satellites. The GPS Control Segment routinely generates navigation message data on the basis of a prediction model and the measurements at more than a dozen monitor stations. The differences between the broadcast ephemerides/clocks and the truth account for signal-in-space (SIS) errors. SIS errors are usually undetectable and uncorrectable for stand-alone SPS users, and hence directly affect the positioning accuracy and integrity. Nominally, SPS users can assume that each broadcast navigation message is reliable and the user range error (URE) derived from a healthy SIS is at the meter level or even sub-meter level. In practice, unfortunately, SIS anomalies have happened occasionally and UREs of tens of meters or even more have been observed, which can result in an SPS receiver outputting a hazardously misleading position solution. Receiver autonomous integrity monitoring (RAIM) or advanced RAIM is a promising tool to protect stand-alone users from such hazards; however, most RAIM algorithms assume at most one satellite fault at a time. Knowledge about the SIS anomalies in history is very important not only for assessing the GPS SIS integrity performance but also for validating the fundamental assumption of RAIM.

    A typical method for calculating SIS UREs is to compare the broadcast ephemerides/clocks with the precise, post-processed ones. Although this method is very effective in assessing the GPS SIS accuracy performance, few attempts have been made to use it to assess the GPS SIS integrity performance because broadcast ephemeris/clock data obtained from a global tracking network sometimes contain errors caused by receivers or data conversion processes and these errors usually result in false SIS anomalies. In this article, we introduce a systematic methodology to cope with this problem and screen out all the potential SIS anomalies in the past decade from when Selective Availability (SA) was turned off.

    GPS SIS Integrity

    The integrity of a navigation system refers — just as it does to a person — to its honesty, veracity, and trustworthiness. In the case of GPS, this includes the integrity of the ephemeris and clock data in the broadcast navigation messages. We refer to this as signal-in-space integrity.

    GPS SIS URE. As indicated by the name, GPS SIS URE is the pseudorange modeling inaccuracy due to operations of the GPS ground control and the space vehicles. Specifically, SIS URE includes satellite ephemeris and clock errors, satellite antenna performance variations, and signal imperfections, but not ionospheric or tropospheric delay, multipath, or any errors due to user receivers. SIS URE is dominated by ephemeris and clock errors because antenna variations and signal imperfections are at a level of millimeters or centimeters.

    In broadcast navigation messages, there is a parameter called user range accuracy (URA) that is intended to be a conservative representation of the standard deviation (1-sigma) of the URE at the worst-case location on the Earth. For example, a URA index value of 0 means that the 1-sigma URE is expected to be less than 2.4 meters, and a URA index value of 1 means that the 1-sigma URE is expected to be greater than 2.4 meters but less than 3.4 meters, and so on. In the past several years, most GPS satellites have a URA index value of 0. A nominal URA value, in meters, can be computed as X = 2(1+N/2), where N is the index value, for index values of 6 or less. For 6 < N < 15, X = 2(N-2).

    GPS SPS SIS Integrity. In the SPS Performance Standard (PS), as well as the latest version of the Interface Specification (IS-GPS-200E), the GPS SPS SIS URE integrity standard assures that for any healthy SIS, there is an up-to-10−5 probability over any hour of the URE exceeding the not-to-exceed (NTE) tolerance without a timely alert during normal operation. The NTE tolerance is currently defined to be 4.42 times the upper bound (UB) on the URA value broadcast by the satellite. Before September 2008, the NTE tolerance was defined differently, as the maximum of 30 meters and 4.42 times URA UB. The reason for the “magic” number 4.42 here is the Gaussian assumption of the URE, although this assumption may be questionable. (4.42 sigma corresponds to a probability level of 99.999 percent (1 – 10–5)).

    In this article, a GPS SPS SIS anomaly is defined as a threat of an SIS integrity failure; that is, a condition during which an SPS SIS marked healthy results in a URE exceeding the NTE tolerance. Because the definition of the NTE tolerance is different before and after September 2008, we consider both of the two NTE tolerances for the sake of completeness and consistency.

    Methodology

    The SIS anomalies are screened out by comparing broadcast ephemerides/clocks with precise ones. As shown in Figure 1, the whole process consists of three steps: data collecting, data cleansing, and anomaly screening.

    Inn-Fig1 Source: Richard Langley
    Figure 1. Framework of the whole process. XYZB values refer to the coordinates of satellite position and satellite clock bias.

    In the first step, the navigation message data files are downloaded from the International GNSS Service (IGS). In addition, two different kinds of precise ephemeris/clock data are downloaded from IGS and the National Geospatial-Intelligence Agency (NGA), respectively. The details about these data sources will be discussed in the next section.

    Since each GPS satellite can be observed by many IGS stations at any instant, each navigation message is recorded redundantly. In the second step, a data-cleansing algorithm exploits the redundancy to remove the errors caused on the ground. This step distinguishes our work from that of most other researchers because the false anomalies due to corrupted data can be mostly precluded.

    The last step is computing worst-case SIS UREs as well as determining potential SIS anomalies. The validated navigation messages prepared in the second step are used to propagate broadcast orbits/clocks at 15-minute intervals that coincide with the precise ones. A potential SIS anomaly is claimed when the navigation message is healthy and in its fit interval with the worst-case SIS URE exceeding the SIS URE NTE tolerance.

    Data Sources

    We obtained broadcast navigation message data and precise ephemeris and clock data from publicly available sources.

    Broadcast Navigation Message Data. Broadcast GPS navigation message data files are available at IGS Internet sites. All the data are archived in Receiver Independent Exchange (RINEX) navigation file format, which includes not only the ephemeris/clock parameters broadcast by the satellites but also some information produced by the ground receivers, such as the pseudorandom noise (PRN) signal number and the transmission time of message (TTOM).

    The IGS tracking network is made up of more than 300 volunteer stations all over the world (a map is shown in Table 1) ensuring seamless, redundant data logging. Since broadcast navigation messages are usually updated every two hours, no single station can record all navigation messages. For the ease of users, two IGS archive sites, the Crustal Dynamics Data Information System (CDDIS) and the Scripps Orbit and Permanent Array Center (SOPAC), provide two kinds of ready-to-use daily global combined broadcast navigation message data files, brdcddd0.yyn and autoddd0.yyn, respectively, where ddd is the day of year yy. Unfortunately, these files sometimes contain errors that can cause false anomalies.

    Table 1. Comparison of IGS and NGA precise ephemeris/clock data. Source: Richard Langley
    Table 1. Comparison of IGS and NGA precise ephemeris/clock data.

    Therefore, we devised and implemented a data-cleansing algorithm to generate the daily global combined navigation messages, which are as close as possible to the navigation messages that the satellites actually broadcast, from all available navigation message data files of all IGS stations. The data-cleansing algorithm is based on majority vote, and hence all values in our data are cross validated. Accordingly, we name our daily global combined navigation messages “validated navigation messages,” as shown in Figure 1.

    Precise Ephemeris and Clock Data. Precise GPS ephemerides/clocks are generated by some organizations such as IGS and NGA that routinely post-process observation data. Precise ephemerides/clocks are regarded as “truth” because of their centimeter-level accuracy.

    Table 1 shows a side-by-side comparison between IGS and NGA precise ephemeris/clock data, in which the green- and red-colored text implies pros and cons, respectively. For NGA data, the only con is that the data have been publicly available only since January 4, 2004. As a result, for the broadcast ephemerides/clocks before this date, IGS precise ephemerides/clocks are the only references. Nevertheless, care must be taken when using IGS precise ephemerides/clocks due to the following three issues.

    The first issue with the IGS precise ephemerides/clocks is the relatively high rate of bad/absent data, as shown in the third row of Table 1. For a GPS constellation of 27 healthy satellites, 1.5 percent bad/absent data means no precise ephemerides or clocks for approximately 10 satellite-hours per day. This issue can result in undetected anomalies (false negatives).

    The second issue is that, as shown in the fourth row of Table 1, IGS switched to IGS Time for its precise ephemeris/clock data on 22 February, 2004. The IGS clock is not synchronized to GPS Time, and the differences between the two time references may be as large as 3 meters. Fortunately, the time offsets can be extracted from the IGS clock data files. Moreover, a similar problem is that IGS precise ephemerides use a frame aligned to the International Terrestrial Reference Frame (ITRF) whereas broadcast GPS ephemerides are based on the World Geodetic System 1984 (WGS 84). The differences between ITRF and the versions of WGS 84 used since 1994 are on the order of a few centimeters, and hence a transformation is not considered necessary for the purpose of our work.

    The last, but not the least important, issue with the IGS precise ephemerides is that the data are provided only for the center of mass (CoM) of the satellite. Since the broadcast ephemerides are based on the satellite antenna phase center (APC), the CoM data must be converted to the APC before being used. Both IGS and NGA provide antenna corrections for every GPS satellite. Although the IGS and the NGA CoM data highly agree with each other, the IGS satellite antenna corrections are quite different from the NGA’s, and the differences in z-offsets can be as large as 1.6 meters for some GPS satellites. The reason for these differences is mainly due to the different methods in producing the antenna corrections: the IGS antenna corrections are based on the statistics from more than 10 years of IGS data, whereas the NGA’s are probably from the calibration measurements on the ground. In order to know whose satellite antenna corrections are better, the broadcast orbits for all GPS satellites in 2009 were computed and compared with three different precise ephemerides: IGS CoM + IGS antenna corrections, IGS CoM + NGA antenna corrections, and NGA APC. Generally, the radial ephemeris error is expected to have a zero mean. However, the combination “IGS CoM + IGS antenna corrections” results in radial ephemeris errors with a non-zero mean for more than half of the GPS satellites. Therefore, the NGA antenna corrections were selected to convert the IGS CoM data to the APC.

    Data Cleansing

    Figure 2 shows a scenario of data cleansing. Owing to accidental bad receiver data and various hardware/software bugs, a small proportion of the navigation data files from the IGS stations have defects such as losses, duplications, inconsistencies, discrepancies, and errors. Therefore, more than just removing duplications, the generation of validated navigation messages is actually composed of two complicated steps.

    Figure 2. A scenario of data cleansing: In the figure, the GPS satellite PRN32 started to transmit a new navigation message at 14:00. Receiver 1 had not observed the satellite until 14:36, and hence the TTOM in its record was 14:36. Additionally, Receiver 1 made a one-bit error in ∆n (4.22267589140 × 10-9 11823 × 2−43 π). Receiver 2 perhaps had some problems in its software: the IODC was unreported and both the toc and ∆n were written weirdly. Receiver n used an incorrect ranging code, PRN01, to despread and decode the signal of PRN32; fortunately, all the parameters except TTOM were perfectly recorded. Moreover, the three receivers interpreted URA (SV accuracy) differently. A computer equipped with our data cleansing algorithms is used to process all the data from the receivers. The receiver-caused errors are removed and the original navigation message is recovered. Source: Richard Langley
    Figure 2. A scenario of data cleansing: In the figure, the GPS satellite PRN32 started to transmit a new navigation message at 14:00. Receiver 1 had not observed the satellite until 14:36, and hence the TTOM in its record was 14:36. Additionally, Receiver 1 made a one-bit error in ∆n (4.22267589140 × 10-9 11823 × 2−43 π). Receiver 2 perhaps had some problems in its software: the IODC was unreported and both the toc and ∆n were written weirdly. Receiver n used an incorrect ranging code, PRN01, to despread and decode the signal of PRN32; fortunately, all the parameters except TTOM were perfectly recorded. Moreover, the three receivers interpreted URA (SV accuracy) differently. A computer equipped with our data cleansing algorithms is used to process all the data from the receivers. The receiver-caused errors are removed and the original navigation message is recovered.

    First step. Suppose that we want to generate the validated navigation messages for day n. In the first step, we apply the following operations sequentially to each RINEX navigation data file from day n − 1 to day n + 1:

    1) Parse the RINEX navigation file;

    2) Recover least significant bit (LSB);

    3) Classify URA values;

    4) Remove the navigation messages not on day n;

    5) Remove duplications;

    6) Add all remaining navigation messages into the set O.

    The reason why the data files from day n − 1 to day n + 1 are considered is that a few navigation messages around 00:00 can be included in some data files on day n − 1, and a few navigation messages around 23:59 can be included in some data files on day n + 1. The LSB recovery is used here to cope with the discrepant representations of floating-point numbers in RINEX navigation files. The URA classifier is employed to recognize and unify various representations of URA in the files. The duplication removal is applied because some stations write the same navigation messages repeatedly in one data file, which is unfavorable to the vote in the second step.

    Second Step. At the end of the first step, we have a set O that includes all the navigation messages on day n. The set O still has duplications because a broadcast navigation message can be reported by many IGS stations. However, as shown in Figure 2, duplications of a broadcast navigation message may come with different errors and are not necessarily identical. Several other examples of such problems can be found in our journal paper listed in Further Reading. Fortunately, most orbital and clock parameters are seldom reported incorrectly, and even when errors happen, few stations agree on the same incorrect value. In our work, these parameters are referred to as robust parameters. On the contrary, some parameters, such as TTOM, PRN, URA and issue of data clock (IODC), are more likely to be erroneous and when errors happen, several stations may make the same mistake. These parameters are referred to as fragile parameters. The cause of the fragility is either the physical nature (for example, TTOM, PRN) or the carelessness in hardware/software implementations (for example, URA, IODC).

    Majority vote is applied to all fragile parameters (except TTOM, which is determined by another algorithm described in our journal paper) under the principle that the majority is usually correct. Meanwhile, the robust parameters are utilized to identify the equivalence of two navigation messages — two navigation messages are deemed identical if and only if they agree on all the robust parameters, although their fragile parameters could be different. Therefore, the goal of duplication removal and majority vote is a set P, in which any navigation message must have at least one robust parameter different from any other and has all fragile parameters confirmed by the largest number of stations that report this navigation message.

    After the operations above, we have a set P in which there are no duplicated navigation messages in terms of robust parameters and all fragile parameters are as correct as possible. A few navigation messages in P still have errors in their robust parameters. These unwanted navigation messages feature a small number of reporting stations. Finally, the navigation messages confirmed by only a few stations being discarded and the survivors are the validated broadcast navigation messages, stored in files sugldddm.yyn. For further details of our algorithms, see our journal paper.

    Anomaly Screening

    The validated broadcast navigation messages prepared using the algorithm described in the previous section were employed to propagate broadcast satellite orbits and clocks. For each 15-miniute epoch, t, that coincides with precise ephemerides/clocks, the latest transmitted broadcast ephemeris/clock is chosen to calculate the worst-case SIS URE – the maximum SIS URE that a user on Earth can experience.

    Finally, a potential GPS SIS anomaly is claimed when all of the following conditions are fulfilled.

    • The worst-case SIS URE exceeds the NTE tolerance;
    • The broadcast navigation message is healthy; that is,
      • The RINEX field SV health is 0, and
      • The URA UB ≤ 48 meters;
    • The broadcast navigation message is in its fit interval; that is, ∆t = t − TTOM ≤ 4 hours;
    • The precise ephemeris/clock is available and healthy.

    Results

    A total of 397,044,414 GPS navigation messages collected by an average of 410 IGS stations from June 1, 2000 (one month after turning off SA), to August 31, 2010, have been screened. The NGA APC precise ephemerides/clocks and the IGS CoM precise ephemerides/clocks with the NGA antenna corrections were employed as the truth references. Both old and new NTE tolerances were used for determining anomalies.

    Before interpreting the results, it should be noted that there are some limitations due to the data sources and the anomaly-determination criteria. First, false anomalies may be claimed because there may be some errors in the precise ephemerides/clocks or the validated navigation messages. Second, some short-lived anomalies may not show up if they happen to fall into the 15-minute gaps of the precise ephemerides/clocks. Third, some true anomalies may not be detected if the precise ephemerides/clocks are temporarily missing. The third limitation is especially significant for the results before January 3, 2004, because only the IGS precise ephemerides/clocks are available, which feature a high rate of bad/absent data. (For example, the clock anomaly of Space Vehicle Number (SVN) 23/PRN23 that occurred on January 1, 2004 is missed by our process because the IGS precise clocks for PRN23 on that day were absent.) Last but not least, users might not experience some anomalies because a satellite was not trackable at that time, or the users were notified via a Notice Advisory to Navstar Users (NANU). (A satellite may indicate that it is unhealthy through the use of non-standard code or data. The authors’ future work will include using observation data to verify the potential anomalies found in the results presented here.) Therefore, all the SIS anomalies claimed in this article are considered to be potential and under further investigation.

    Potential SIS Anomalies. A total of 1,256 potential SIS anomalies were screened out under SPS PS 2008 (or 374 potential SIS anomalies under SPS PS 2001). Figure 3 shows all these anomalies in a Year-SVN plot. It can be seen that during the first year after SA was turned off, SIS anomalies occurred frequently for the whole constellation.

    Figure 3. Potential SIS anomalies from June 1, 2000, to August 31, 2010. The horizontal lines depict the periods when the satellites were active (not necessarily healthy). The color of the lines indicates the satellites' block type, as explained by the top left legend. Source: Richard Langley
    Figure 3. Potential SIS anomalies from June 1, 2000, to August 31, 2010. The horizontal lines depict the periods when the satellites were active (not necessarily healthy). The color of the lines indicates the satellites’ block type, as explained by the top left legend.

    Moreover, 2004 is apparently a watershed: before 2004, anomalies occurred for all GPS satellites (except two satellites launched in 2003, SVN45/PRN21 and SVN56/PRN16) whereas after 2004, anomalies occurred much less frequently and more than 10 satellites have never been anomalous. Figure 4 further confirms the improving GPS SIS integrity performance in the past decade, no matter which SPS PS is considered.

    Figure 4. Number of potential SIS anomalies per year. The SIS performance was improved during the past decade. There were 0 anomalies in 2009 according to SPS PS 2001 and this number is represented by 0.1 in the figure. Source: Richard Langley
    Figure 4. Number of potential SIS anomalies per year. The SIS performance was improved during the past decade. There were 0 anomalies in 2009 according to SPS PS 2001 and this number is represented by 0.1 in the figure.

    Therefore, it is possible to list all potential SIS anomalies from January 4, 2004, to August 31, 2010, in a compact table: Table 2. Most anomalies in the table have been confirmed by NANUs and other literature. The table reveals an important and exciting piece of information: never have two or more SIS anomalies occurred simultaneously since 2004. Accordingly, in the sense of historical GPS SIS integrity performance, it is valid for RAIM to assume at most one satellite fault at a time.

    Table 2. List of potential anomalies from January 4, 2004, to August 31, 2010. Source: Richard Langley
    Table 2. List of potential anomalies from January 4, 2004, to August 31, 2010.

    Validated Navigation Messages. For the purpose of comparison and verification, the IGS daily global combined broadcast navigation message data files brdcddd0.yyn and autoddd0.yyn were used to propagate broadcast satellite orbits and clocks as well. The NGA APC precise ephemerides/clocks were employed for the truth references. The SPS PS 2008 NTE tolerance was used for determining anomalies. The other criteria for anomaly screening that are the same as in the previous section were still applied.

    All the potential SIS anomalies for 2006–2009 were found based on the three kinds of daily combined broadcast navigation messages. Table 3 shows a comparison of the total hours of the anomalies per year. It can be seen that brdcddd0.yyn and autoddd0.yyn result in approximately 11 times more false anomalies than true ones. Moreover, all potential anomalies derived from sugldddm.yyn are confirmed by brdcddd0.yyn and autoddd0.yyn, which indicates that our sugldddm.yyn does not introduce any more false anomalies than brdcddd0.yyn and autoddd0.yyn.

    Table 3. Total hours of anomalies per year computed from three different kinds of daily global combined broadcast navigation messages. Source: Richard Langley
    Table 3. Total hours of anomalies per year computed from three different kinds of daily global combined broadcast navigation messages.

    Conclusion

    In this article, the GPS SIS integrity performance in the past decade was assessed by comparing the broadcast ephemerides/clocks with the precise ones. Thirty potential anomalies were found. The fundamental assumption of RAIM is valid based on a review of the GPS SIS integrity performance in the past seven years.

    Acknowledgments

    The authors gratefully acknowledge the support of the Federal Aviation Administration. This article contains the personal comments and beliefs of the authors, and does not necessarily represent the opinion of any other person or organization.

    The authors would like to thank Mr. Tom McHugh, William J. Hughes FAA Technical Center, for his valuable input to the data-cleansing algorithm. This article is based on the paper “GPS Signal-in-Space Integrity Performance Evolution in the Last Decade: Data Mining 400,000,000 Navigation Messages from a Global Network of 400 Receivers” to appear in the Institute of Electrical and Electronics Engineers (IEEE) Transactions on Aerospace and Electronic Systems..

    Liang Heng is a Ph.D. candidate under the guidance of Professor Per Enge in the Department of Electrical Engineering at Stanford University.

    Grace Xingxin Gao is a research associate in the GPS Research Laboratory of Stanford University.

    Todd Walter is a senior research engineer in the Department of Aeronautics and Astronautics at Stanford University.

    Per Enge is a professor of Aeronautics and Astronautics at Stanford University, where he is the Kleiner-Perkins, Mayfield, Sequoia Capital Professor in the School of Engineering. He directs the GPS Research Laboratory, which develops satellite navigation systems based on GPS.

    FURTHER READING

    • Authors’ Research Papers

    “GPS Signal-in-Space Integrity Performance Evolution in the Last Decade: Data Mining 400,000,000 Navigation Messages from a Global Network of 400 Receivers” by L. Heng, G.X. Gao, T. Walter, and P. Enge in Transactions on Aerospace and Electronic Systems, the Institute of Electrical and Electronics Engineers, accepted for publication.

    “GPS Signal-in-Space Anomalies in the Last Decade: Data Mining of 400,000,000 GPS Navigation messages” by L. Heng, G.X. Gao, T. Walter, and P. Enge in Proceedings of ION GNSS 2010, the 23rd International Technical Meeting of The Satellite Division of the Institute of Navigation, Portland, Oregon, September 21–24, 2010, pp. 3115–3122.

    “GPS Ephemeris Error Screening and Results for 2006–2009” by L. Heng, G.X. Gao, T. Walter, and P. Enge in Proceedings of ION ITM 2010, the 2010 International Technical Meeting of the Institute of Navigation, San Diego, California, January 24–26, 2010, pp. 1014–1022.

    • Earlier Work on Assessing GPS Broadcast Ephemerides and Clocks

    “GPS Orbit and Clock Error Distributions” by C. Cohenour and F. van Graas in Navigation, Vol. 58, No. 1, Spring 2011, pp. 17–28.

    “Statistical Characterization of GPS Signal-in-Space Errors” by L. Heng, G.X. Gao, T. Walter, and P. Enge in Proceedings of ION ITM 2011, the 2011 International Technical Meeting of the Institute of Navigation, San Diego, California, January 24–26, 2011, pp. 312–319.

    “Broadcast vs. Precise GPS Ephemerides: A Historical Perspective” by D.L.M. Warren and J.F. Raquet in GPS Solutions, Vol. 7, No. 3, 2003, pp. 151–156, doi: 10.1007/s10291-003-0065-3.

    “Accuracy and Consistency of Broadcast GPS Ephemeris Data” by D.C. Jefferson and Y.E. Bar-Sever in Proceedings of ION GPS-2000, the 13th International Technical Meeting of the Satellite Division of The Institute of Navigation, Salt Lake City, Utah, September 19–22, 2000, pp. 391–395.

    “The GPS Broadcast Orbits: An Accuracy Analysis” by R.B. Langley, H. Jannasch, B. Peeters, and S. Bisnath, presented in Session B2.1-PSD1, New Trends in Space Geodesy at the 33rd COSPAR Scientific Assembly, Warsaw, July 16–23, 2000.

    • Signal-in-Space Anomalies

    “GNSS: The Present Imperfect” by D. Last in Inside GNSS, Vol. 5, No. 3, May 2010, pp. 60–64.

    “Investigation of Upload Anomalies Affecting IIR Satellites in October 2007” by K. Kovach, J. Berg, and V. Lin in Proceedings of ION GNSS 2008, the 21st International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Georgia, September 16–19, 2008, pp. 1679–1687.

    Global Positioning System (GPS) Standard Positioning Service (SPS) Performance Analysis Report No. 58, July 31, 2007, Reporting Period: 1 April – 30 June 2007.

    Discrepancy Report, DR No. 55, “GPS Satellite PRN18 Anomaly Affecting SPS Performance” by N. Vary, FAA William J. Hughes Technical Center, Pomona, New Jersey, April 11, 2007.

    “GPS Receiver Responses to Satellite Anomalies” by J.W. Lavrakas and D. Knezha in Proceedings of the 1999 National Technical Meeting of The Institute of Navigation, San Diego, California, January 25–27, 1999, pp. 621–626.

    • GPS Integrity and Receiver Autonomous Integrity Monitoring

    “Prototyping Advanced RAIM for Vertical Guidance” by J. Blanch, M.J. Choi, T. Walter, P. Enge, and K. Suzuki in Proceedings of ION GNSS 2010, the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 21–24, 2010, pp. 285–291.

    “The Integrity of GPS” by R.B. Langley in GPS World, Vol. 10, No. 3, March 1999, pp. 60–63.

    • International GNSS Service Ephemerides and Clocks

    “On the Precision and Accuracy of IGS Orbits” by J. Griffiths and J.R. Ray in Journal of Geodesy, Vol. 83, 2009, pp. 277–287, doi: 10.1007/s00190-008-0237-6.

    “The International GNSS Service: Any Questions?” by A.W. Moore in GPS World, Vol. 18, No. 1, January 2007, pp. 58–64.

    International GNSS Service Central Bureau website.

    • National Geospatial-Intelligence Agency Ephemerides and Clocks

    “NGA’s Role in GPS” by B. Wiley, D. Craig, D. Manning, J. Novak, R. Taylor, and L. Weingarth in Proceedings of ION GNSS 2006, the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, September 26–29, 2006, pp. 2111–2119.

    National Geospatial-Intelligence Agency, Geoint Sciences Office, Global Positioning System Division website.

    • Antenna Phase Center Corrections

    “Generation of a Consistent Absolute Phase-center Correction Model for GPS Receiver and Satellite Antennas” by R. Schmid, P. Steigenberger, G. Gendt, M. Ge, and M. Rothacher in Journal of Geodesy, Vol. 81, No. 12, 2007, pp. 781–798, doi: 10.1007/s00190-007-0148-y.

    “The Block IIA Satellite: Calibrating Antenna Phase Centers” by G.L. Mader and F.M. Czopek in GPS World, Vol. 13, No. 5, May 2002, pp. 40–46.

    • GPS Interface and Performance Specifications

    Navstar GPS Space Segment / Navigation User Interfaces, Interface Specification, IS-GPS-200 Revision E, prepared by Science Applications International Corporation, El Segundo, California, for Global Positioning System Wing, June 2010.

    Global Positioning System Standard Positioning Service Performance Standard, 4th edition, by the U.S. Department of Defense, Washington, D.C., September 2008.

    Global Positioning System Standard Positioning Service Performance Standard, 3rd edition, by the U.S. Department of Defense, Washington, D.C., October 2001.

  • Innovation: Filling in the Gaps

    Innovation: Filling in the Gaps

    Improving Navigation Continuity Using Parallel Cascade Identification

    By Umar Iqbal, Jacques Georgy, Michael J. Korenberg, and Aboelmagd Noureldin

    To reliably navigate with fewer than four satellites, GPS pseudoranges needs to be augmented with measurements from other sensors, such as a reduced inertial sensor system or RISS. What is the best way to combine the RISS measurements with the GPS measurements? The classic approach is to integrate the measurements in a conventional tightly coupled Kalman filter. But in this month’s column, we look at how a mathematical procedure called parallel case identification can improve the Kalman filter’s job, when navigating with three, two, one, or even no GPS satellites.

    GPS World photo
    INNOVATION INSIGHTS by Richard Langley

    THREE, TWO, ONE, ZERO! Can you still navigate with just a GPS receiver when the number of tracked GPS satellites drops from four to none? As we know, pseu- doranges from a minimum of four satellites, preferably well spaced out in the sky, are required for three-dimensional positioning. That’s because there are four unknowns to estimate: the three position coordinates (latitude, longitude, and height) and the offset of the receiver clock from GPS System Time. If we had a stable clock in the receiver, then we could hold the clock offset constant and have 3D navigation with just three satellites. But for every 3 nanoseconds of clock drift, we will have about 1 meter of pseudorange error, which will lead to several meters of position error depend- ing on the receiver-satellite geometry. On the other hand, we could hold the height coor- dinate constant (viable for navigation over slowly changing topography or at sea) and estimate the horizontal coordinates and the receiver clock offset. So far, so good.

    What if the number of tracked satellites then drops to two? We can now only esti- mate two unknowns. They could be the two horizontal coordinates, if we hold both the receiver clock offset and the height fixed. Any errors in those fixed values will propagate into the estimated horizontal coordinates but the resulting position errors might still be acceptable. Instead of holding the clock offset
    fixed, we could assume a constant heading and compute the position along the assumed trajectory. However, navigation will rapidly deteriorate as soon as we make the first turn. And one satellite? We would have to make assumptions about the vehicle trajectory, the height, and the clock offset, with likely very poor results. And with no satellites? We might be able to navigate over a short period of time by “dead reckoning,” assuming a constant trajectory and speed, but the resulting positions will be educated guesses at best.

    Clearly, if we want to reliably navigate with fewer than four satellites we need to augment the GPS pseudoranges with measurements from some other sensors. A common approach is to use inertial measurement units or IMUs. A complete IMU consists of three accelerometers and three gyroscopes, and small, cost-effective microelectromechanical IMUs are readily available. For land navigation, however, it can be advantageous to use a reduced inertial sensor system or RISS, consisting of one single-axis gyroscope, two accelerometers, and the vehicle speedometer. We can also make use of GPS pseudorange rates along with the pseudoranges.

    But what is the best way to combine the RISS measurements with the GPS measurements? The classic approach is to integrate the measurements in a conventional tightly coupled Kalman filter. But in this month’s column, we look at how a mathematical procedure called parallel cascade identification can improve the Kalman filter’s job, when navigating with three, two, or even one GPS satellite.

    The Global Positioning System meets the requirements for numerous navigational applications when there is direct line-of-sight (LOS) to four or more GPS satellites. Vehicular navigation systems and personal positioning systems may suffer from satellite signal blockage as LOS to at least four satellites may not be readily available when operating in urban landscapes with high buildings, underpasses, and tunnels, or in the countryside with thick forested areas. In such environments, a typical GPS receiver will have difficulties attaining and maintaining signal tracking, which causes GPS outages resulting in degraded or non-existent positioning information. Due to these well-known limitations of GPS, multi-sensor system integration is often employed. By integrating GPS with complementary motion sensors, a solution can be obtained that is often more accurate than that of GPS alone.

    Microelectromechanical systems (MEMS) inertial sensors have enabled production of lower-cost and smaller-size inertial measurement units (IMUs) with little power consumption. A complete IMU is composed of three accelerometers and three gyroscopes. These MEMS sensors have composite error characteristics that are stochastic in nature and difficult to model. In traditional low-cost MEMS-based IMU/GPS integration, a few minutes of degraded GPS signals can cause position errors, which can reach several hundred meters. For full 3D land vehicle navigation, we had earlier proposed a low-cost MEMS-based reduced inertial sensor system (RISS) based on only one single-axis gyroscope, two accelerometers, and the vehicle odometer, and we have integrated this system with GPS. RISS mitigates several error sources in the full-IMU solution; moreover, RISS reduces system cost further due to the use of fewer sensors. Another enhancement can be achieved by using tightly coupled integration, which can provide GPS aiding for a navigation solution when the number of visible satellites is three or lower, removing the foremost requirement of loosely coupled integration, which is visibility of at least four satellites. GPS aiding during limited GPS satellite availability can improve the operation of the navigation system for tightly coupled systems. Thus, in our reseach, a Kalman filter (KF) is used to integrate low-cost MEMS-based RISS with GPS in a tightly coupled scheme.

    The KF employed in tightly coupled RISS/GPS integration utilizes pseudoranges and pseudorange rates measured by the GPS receiver. The accuracy of the position estimates is highly dependent on the accuracy of the range measurements. The tightly coupled solutions presented in the literature assume that the pseudorange measurement, after correcting for ionospheric and tropospheric delays, satellite clock errors, and ephemeris errors, only have errors due to the receiver clock and white noise. These latter two are the only errors modeled inside the measurement model in the tightly coupled solutions presented in the literature. Experimental investigation of the GPS pseudoranges for vehicle trajectories in different areas and for different scenarios showed that, in addition, there are residual correlated errors. Since it has been experimentally proven that there are residual correlated errors in addition to white noise and receiver clock errors, we have proposed using a nonlinear system identification technique called parallel cascade identification (PCI) to model such correlated errors in pseudorange measurements.

    We propose that the PCI model for the residual pseudorange errors be cascaded with a KF since this nonlinear model cannot be included inside the KF measurement model. The normal operation of a KF is as follows: the difference between the measured pseudorange and pseudorange rate from the mth GPS satellite and the corresponding RISS-predicted estimates of pseudorange and pseudorange rate are used as a measurement update for the KF integration, which computes the estimated RISS errors and corrects the mechanization output. We propose the use of a PCI module, where the role of PCI is to model the pseudorange residual errors. When GPS is available, estimated full 3D position, velocity, and attitude are obtained by integrating the MEMS-based RISS with GPS. In parallel, as a background routine, a PCI model is built for each visible satellite to model its pseudorange error. The actual pseudorange of each visible satellite is used as the input to the model; the target output is the error between the corrected pseudorange (calculated based on corrected receiver position from the integrated solution) and the actual pseudorange. This target output provides the reference output to build the PCI model of the pseudorange residual error. Dynamic characteristics between system input and output help to identify a nonlinear PCI model and the algorithm can then achieve a residual pseudorange error model.

    When fewer than four satellites are visible, the identified parallel cascades for the remaining visible satellites will be used to predict the pseudorange errors for these satellites and correct the pseudorange values to be provided to the KF. This improvement of pseudorange measurements will result in a more accurate aiding for RISS, and thus more accurate estimates of position and velocities.

    We examined the performance of the proposed technique by conducting road tests with real-life trajectories using a low-cost MEMS-based RISS. The ultimate check for the proposed system’s accuracy is during GPS signal degradation and blockage. This work presents both downtown scenarios with natural GPS degradation and scenarios with simulated GPS outages where the number of visible satellites was varied from three to zero. The results are examined and compared with KF-only tightly coupled RISS/GPS integration without pseudorange correction using a PCI module. This comparison clearly demonstrates the advantage of using a PCI module for correcting pseudoranges for tightly coupled integration.

    RISS/GPS Integration

    Earlier, we proposed the reduced inertial sensor system to reduce the overall cost of a positioning system for land vehicles without appreciable performance compromise depending on the fact that land vehicles mostly stay in the horizontal plane. It is the gyroscope technology that contributes the most both to the overall cost of an IMU and to the performance of the INS. In RISS mechanization, the heading (azimuth) angle is obtained by integrating the gyroscope measurement, ωz. Since this measurement includes the component of the Earth’s rotation as well as rotation of the local level frame on the Earth’s curved surface, these quantities are removed from the measurement before integration. Assuming relatively small pitch and roll angles for land vehicle applications, we can write the rate of change of the azimuth angle directly in the local level frame as:
    E-1 Source: Richard Langley   (1)
    where ωe is the Earth’s rotation rate, φ is the latitude, ve is the east velocity of the vehicle, h is the altitude of the vehicle and RN is the normal (prime vertical) radius of curvature of the vehicle’s position on the reference ellipsoid.

    The two horizontal accelerometers can be employed for obtaining the pitch and roll angles of the vehicle. Thus, a 3D navigation solution can be achieved to boost the performance of the solution. When the vehicle is moving, the forward accelerometer measures the forward vehicle acceleration as well as the component due to gravity, g. To calculate the pitch angle, the vehicle acceleration derived from the odometer measurements, aod, is removed from the forward accelerometer measurements, fy. Consequently, the pitch angle is computed as:

    E-2 Source: Richard Langley (2)

    Similarly, the transversal accelerometer measures the normal component of the vehicle acceleration as well as the component due to gravity. Thus, to calculate the roll angle, the transversal accelerometer measurement, fx, must be compensated for the normal component of acceleration. The roll angle is then given by:

    E-3 Source: Richard Langley(3)

    The computed azimuth and pitch angles allow the transformation of the vehicle’s speed along the forward direction, vod (obtained from the odometer measurements) to east, north, and up velocities (ve, vn, and vu respectively) as follows:
    E-4 Source: Richard Langley(4)
    where Rlb is the rotation matrix that transforms velocities in the vehicle body frame to the navigation frame. The east and north velocities are transformed and integrated to obtain position in geodetic coordinates (latitude, φ, and longitude, λ). The vertical component of velocity is integrated to obtain altitude, h. The following equation shows these operations:
    E-5 Source: Richard Langley(5)

    where, in addition to the terms already defined, RM is the meridional radius of curvature. We have used the KF as the estimation technique for tightly coupled RISS/GPS integration in our approach. KF is an optimal estimation tool that provides a sequential recursive algorithm for the optimal least mean variance (LMV) estimation of the system states. In addition to its benefits as an optimal estimator, the KF provides real-time statistical data related to the estimation accuracy of the system states, which is very useful for quantitative error analysis. The filter generates its own error analysis with the computation of the error covariance matrix, which gives an indication of the estimation accuracy.

    In tightly coupled RISS/GPS system architecture, instead of using the position and velocity solution from the GPS receiver as input for the fusion algorithm, raw GPS observations such as pseudoranges and Doppler shifts are used. The range measurement is usually known as a pseudorange due to the contamination of errors, such as atmospheric errors, as well as synchronization errors between the satellite and receiver clocks.

    After correcting for the satellite clock error and the ionospheric and tropospheric errors, we can obtain a corrected pseudorange. The receiver clock error and the residual errors remaining in the corrected pseudorange, assumed as white Gaussian noise, are the only errors modeled inside the measurement model in the tightly coupled solutions presented in the literature. Experimental investigation of the GPS pseudoranges in trajectories in different areas and under different scenarios showed that the residual errors are not just white noise as assumed in the literature, but, in fact, are correlated errors. As the GPS observables are used to update the KF, a technique must be developed to adequately model these errors to improve the overall performance of the KF. We propose using PCI to model these correlated errors. A PCI module models these errors, and then provides corrections prior to sending the GPS pseudoranges to aid the KF during periods of GPS partial outages (when the number of visible satellites drops below four).

    Parallel Cascade Identification

    What is PCI? System identification is a procedure for inferring the dynamic characteristics between system input and output from an analysis of time-varying input-output data. Most of the techniques assume linearity due to the simplicity of analysis since nonlinear techniques make analysis much more complicated and difficult to implement than for the linear case. However, for more realistic dynamic characterization nonlinear techniques are suggested. PCI is a nonlinear system identification technique proposed by one of us [MJK]. This technique models the input/output behavior of a nonlinear system by a sum of parallel cascades of alternating dynamic linear (L) and static nonlinear (N) elements. The parallel array shown in Figure 1 can be built up one cascade at a time.

    Figure 1. Block diagram of parallel cascade identification. Source: Richard Langley
    Figure 1. Block diagram of parallel cascade identification.

    It has been proven that any discrete-time Volterra series with finite memory and anticipation can be uniformly approximated by a finite sum of parallel LNL cascades, where the static nonlinearities, N, are exponentials and logarithmic functions. [A Volterra series, named after the Italian mathematician and physicist Vito Volterra, is similar to the more familiar infinite Taylor series expansion of a function used, for example, in systems analysis, but the Volterra series can include system “memory” effects.] It has been shown that any discrete-time doubly finite (finite memory and order) Volterra series can be exactly represented by a finite sum of LN cascades where the N are polynomials. A key advantage of this technique is that it is not dependent on a white or Gaussian input, but the identified individual L and N elements may vary depending on the statistical properties of the input chosen. The cascades can be found one at a time and nonlinearities in the models are localized in static functions. This reduces the computation as higher order nonlinearities are approximated using higher degree polynomials in the cascades rather than higher order kernels in a Volterra series approximation.

    The method begins by approximating the nonlinear system by a first such cascade. The residual (that is, the difference between the system and the cascade outputs) is treated as the output of a new nonlinear system, and a second cascade is found to approximate the latter system, and thus the parallel array can be built up one cascade at a time. Let yk(n) be the residual after fitting the kth cascade, with yo(n) = y(n). Let zk(n) be the output of the kth cascade, so
    E-6 Source: Richard Langley(6)
    where k = 1, 2, …

    The dynamic linear elements in the cascades can be determined in a number of ways. The method we have employed uses cross correlations of the input with the current residual. Best fitting of the current residuals was used to find the polynomial coefficients of the static nonlinearities. These resulting cascades are such that they drive the cross-correlations of the input with the residuals to zero. However, a few basic parameters have to be specified in order to identify a parallel cascade model, including the memory length of the dynamic linear element that begins each cascade, the degree of the polynomial static nonlinearity that follows the linear element (this polynomial is best fit to minimize the mean-square error (MSE) of the approximation of the residual), the maximum number of cascades allowable in the model, and a threshold based on a standard correlation test for determining whether a cascade’s reduction of the MSE justifies its addition to the model.

    Augmented Kalman Filter

    In the previous section, the parallel cascade model was briefly presented, together with a simple method for building up the model to approximate the behavior of a dynamic nonlinear system, given only its input and output. In order to apply PCI to 3D RISS/GPS integration, we propose the use of a KF-PCI module, where the role of PCI is to model the residual errors of GPS pseudoranges.

    In full GPS coverage when four or more satellites are available to the GPS receiver, the KF integrated solution provides an adequate position benefiting from both GPS and RISS readings, and the PCI builds the model for the pseudorange errors for each visible satellite. The input of each PCI module is the pseudorange of the visible mth GPS satellite, and the reference output is the difference between the observed pseudorange and the estimated pseudorange from the corrected navigation solution.

    The reference output has no corrections during full GPS coverage. It is only used to build the PCI model. Dynamic characteristics between system input and output help to achieve a residual pseudorange error model as shown in the Figure 2.

    Figure 2. Block diagram of augmented KF-PCI module for pseudoranges during GPS availability. Source: Richard Langley
    Figure 2. Block diagram of augmented KF-PCI module for pseudoranges during GPS availability.

    During partial GPS coverage, when there are fewer than four satellites available, the PCI modules for all satellites cease training, and the available PCI model for each visible satellite is used to predict the corresponding residual pseudorange errors, as shown in Figure 3. The KF operates as usual, but in this instance the GPS observed pseudorange is corrected by the output of the corresponding PCI. The pre-built PCI models, only for the visible satellites during the partial outage, predict the corresponding residual pseudorange errors to obtain a correction. Thus, the corrected pseudorange can then be obtained.

    During a full GPS outage, when no satellites are available, the KF operates in prediction mode and the PCI modules neither provide corrections nor operate in training mode.

    FIGURE 3 Block diagram of augmented KF-PCI module for pseudoranges during limited availability of GPS. Source: Richard Langley
    FIGURE 3 Block diagram of augmented KF-PCI module for pseudoranges during limited availability of GPS.

    Experimental Setup

    The performance of the developed navigation solution was examined with road test experiments in a land vehicle. The experimental data collection was carried out using a full-size passenger van carrying a suite of measurement equipment that included inertial sensors, GPS receivers, antennae, and computers to control the instruments and acquire the data as shown in the Figure 4. The inertial sensors used in our tests are packaged in a MEMS-grade IMU. The specifications of the IMU are listed in Table 1.

    TABLE 1 IMU specifications. Source: Richard Langley
    Table 1. IMU specifications.

    The vehicle’s forward speed readings were obtained from vehicle built-in sensors through the On-Board Diagnostics version II (OBD II) interface. The sample rate for the collection of speed readings was 1 Hz. The GPS receiver used in our integrated system was a high-end dual-frequency unit. Our results were evaluated with respect to a reference solution determined by a system consisting of another receiver of the same type, integrated with a tactical grade IMU.

    This system provided the reference solution to validate the proposed method and to examine the overall performance during simulated GPS outages.
    Several road test trajectories were carried out using the setup described above. The road test trajectory considered for this article was performed in the city of Kingston, Ontario, Canada, and is shown in Figure 5. This road test was performed for nearly 48 minutes of continuous vehicle navigation and a distance of around 22 kilometers. Ten simulated GPS outages of 60 seconds each were introduced in post-processing (they are shown as blue circles overlaid on the map in Figure 5) during good GPS availability. The trajectory was run four times with the simulated partial outages having three, two, one, and zero visible satellites, respectively. The case with no satellites seen is like a scenario that would occur in loosely coupled integration. The errors estimated by KF-PCI and KF-only solutions were evaluated with respect to the reference solution.

    Experimental Results

    The results in Figure 6 and Figure 7 demonstrate the benefits of the proposed PCI module. The main benefit of using PCI for pseudorange correction is the modeling capability, which enables correction of the raw GPS measurements. The benefit of more satellite availability can also be seen from these results. Figures 6 and 7 clearly show that both the average maximum position error and the average root-mean-square (RMS) position error are lower with the KF-PCI approach compared to the conventional KF, even when data from only one satellite is available.

    FIGURE 6 Bar graph showing average maximum positional errors for all outages. Source: Richard Langley
    Figure 6. Bar graph showing average maximum positional errors for all outages.
    Figure 7. Bar graph for RMS positional errors for all outages. Source: Richard Langley
    Figure 7. Bar graph for RMS positional errors for all outages.

    To gain more insight about the performance of the proposed technique to enhance the aiding of the KF by correcting the pseudoranges, we can look at the results of KF-PCI and KF approaches with different numbers of satellites visible to the receiver for one of the artificial outages. Figure 8 shows a map featuring the different compared solutions during outage number 8. Eight solutions are presented for the cases of three, two, one, and zero satellites observed for the standard KF and KF with PCI. To get some idea of the vehicle dynamics during this outage, we can examine Figure 9, which depicts the forward speed of the vehicle as well as its azimuth angle as obtained from the reference solution. There is a significant variation in speed, with only a small variation in azimuth.

    FIGURE 8 Performance of tightly coupled 3D-RISS during outage #8. Source: Richard Langley
    Figure 8. Performance of tightly coupled 3D-RISS during outage #8.
    ▲ FIGURE 9 Vehicle dynamics (speed and azimuth) during GPS outage #8. Source: Richard Langley
    Figure 9. Vehicle dynamics (speed and azimuth) during GPS outage #8.

    Figure 10 illustrates the performance differences between the KF-PCI and KF-only solutions for different numbers of satellites for this outage. Similar to Figure 7, Figure 10 shows the average RMS position differences between the KF-PCI and KF-only solutions and the reference solution (without the artificial outages). While the differences increase as the number of available satellites decreases, the accuracies may still be acceptable for many navigation purposes.

    And while the differences between the KF-PCI and KF-only approaches for this particular outage are small, the KF-PCI approach consistently provides better accuracy.

    FIGURE 10 Performance of PCI-KF (shades of blue for different number of satellites) and KF (shades of green for different number of satellites) of tightly coupled 3D-RISS during outage #8. Source: Richard Langley
    Figure 10. Performance of PCI-KF (shades of blue for different number of satellites) and KF (shades of green for different number of satellites) of tightly coupled 3D-RISS during outage #8.

    Conclusion

    In this article, we have described a novel design for a navigation system that augments a tightly coupled KF system with PCI modules using low-cost MEMS-based 3D RISS and GPS observations to produce an integrated positioning solution. A PCI module is built for each satellite during good signal availability where the integrated solution presents a good position estimate. The output of each PCI module provides corrections to the GPS pseudoranges of the corresponding visible satellite during GPS partial outages, thereby decreasing residual errors in the GPS observations. This KF-PCI module was tested with real road-test trajectories and compared to a KF-only approach and was shown to improve the overall maximum position error during GPS partial outages.

    Future work with PCI for modeling the residual pseudorange errors will consider replacing the KF with a particle filter to provide more robust integration and a consistent level of accuracy.

    Acknowledgments

    The research discussed in this article was supported, in part, by grants from the Natural Sciences and Engineering Research Council of Canada, the Geomatics for Informed Decisions (GEOIDE) Network of Centres of Excellence, and Defence Research and Development Canada. The equipment was acquired by research funds from the Directorate of Technical Airworthiness and Engineering Support, the Canada Foundation for Innovation, the Ontario Innovation Trust, and the Royal Military College of Canada. The article is based on the paper “Modeling Residual Errors of GPS Pseudoranges by Augmenting Kalman Filter with PCI for Tightly Coupled RISS/GPS Integration” presented at ION GNSS 2010, the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation held in Portland, Oregon, September 21–24, 2010.

    Manufacturers

    The test discussed in this article used a NovAtel Inc. OEM4 dual-frequency GPS receiver and a Crossbow Technology Inc., now Moog Crossbow IMU300CC-100 MEMS-grade IMU. The On-Board Diagnostics data was accessed with a Davis Instruments CarChip Pro data logger. The reference solutions were provided by a NovAtel G2 Pro-Pack SPAN unit, interfacing a NovAtel OEM4 receiver with a Honeywell HG1700 tactical grade IMU.

    Umar Iqbal is a doctoral candidate at Queen’s University, Kingston, Ontario, Canada. He received a master’s of electrical engineering degree in integrated positioning and navigation systems from Royal Military College (RMC)  of Canada, Kingston, in 2008. He also holds an M.Sc. in electronics engineering from the Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan, and a B.Sc. in electrical engineering from the University of Engineering and Technology, Lahore, Pakistan. His research focuses on the development of enhanced performance navigation and guidance systems that can be used in several applications including car navigation.

    Jacques Georgy received his Ph.D. degree in electrical and computer engineering from Queen’s University in 2010. He received B.Sc. and M.Sc. degrees in computer and systems engineering from Ain Shams University, Cairo, Egypt, in 2001 and 2007, respectively. He is working in positioning and navigation systems for vehicular, machinery, and portable applications with Trusted Positioning Inc., Calgary, Alberta, Canada. He is also a member of the Navigation and Instrumentation Research Group at RMC. His research interests include linear and nonlinear state estimation, positioning and navigation by inertial navigation system/global positioning system integration, autonomous mobile robot navigation, and underwater target tracking.

    Michael J. Korenberg is a professor in the Department of Electrical and Computer Engineering at Queen’s University. He holds an M.Sc. (mathematics) and a Ph.D. (electrical engineering) from McGill University, Montreal, Quebec, Canada, and has published extensively in the areas of nonlinear system identification and time-series analysis.

    Aboelmagd Noureldin is a cross-appointment associate professor with the Department of Electrical and Computer Engineering at Queen’s University and the Department of Electrical and Computer Engineering at RMC. He is also the founder and leader of the Navigation and Instrumentation Research Group at RMC. He received the B.Sc. degree in electrical engineering and the M.Sc. degree in engineering physics from Cairo University, Giza, Egypt, in 1993 and 1997, respectively, and the Ph.D. degree in electrical and computer engineering from The University of Calgary, Calgary, Alberta, Canada, in 2002. His research is related to artificial intelligence, digital signal processing, spectral estimation and de-noising, wavelet multiresolution analysis, and adaptive filtering, with emphasis on their applications in mobile multisensor system integration for navigation and positioning technologies.

    FURTHER READING

    ◾ Reduced Inertial Sensing Systems

    Integrated Reduced Inertial Sensor System/GPS for Vehicle Navigation: Multi-sensor Positioning System for Land Applications Involving Single-Axis Gyroscope Augmented with Vehicle Odometer and Integrated with GPS by U. Iqbal and A. Noureldin, published by VDM Verlag Dr. Müller, Saarbrucken, Germany, 2010.

    “A Tightly-Coupled Reduced Multi- Sensor System for Urban Navigation” by T.B. Karamat, J. Georgy, U. Iqbal, and A. Noureldin in Proceedings of ION GNSS 2009, the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Georgia, September 22–25, 2009, pp. 582–592.

    “An Integrated Reduced Inertial Sensor System – RISS/GPS for Land Vehicle” by U. Iqbal, A.F. Okou, and A. Noureldin, in Proceedings of PLANS 2008, IEEE/ION Position Location and Navigation Symposium, Monterey, California, May 5–8, 2008, pp. 912– 922, doi: 0.1109/PLANS.2008.4570075.

    ◾ Integrated Positioning

    “Experimental Results on an Integrated GPS and Multisensor System for Land Vehicle Positioning” by U. Iqbal, T.B. Karamat, A.F. Okou, and A. Noureldin in International Journal of Navigation and Observation, Hindawi Publishing Corporation, Vol. 2009, Article ID 765010, 18 pp., doi: 10.1155/2009/765010.

    “Performance Enhancement of MEMS Based INS/GPS Integration for Low Cost Navigation Applications” by A. Noureldin, T.B. Karamat, M.D. Eberts, and A. El-Shafie in IEEE Transactions on Vehicular Technology, Vol. 58, No. 3, March 2009, pp. 1077–1096, doi: 10.1109/TVT.2008.926076.

    Aided Navigation: GPS with High Rate Sensors by J.A. Farrell, published by McGraw-Hill, New York, 2008.

    Global Positioning Systems, Inertial Navigation, and Integration by M.S. Grewal, L.R. Weill, and A.P. Andrews, 2nd ed., published by Wiley- Interscience, Hoboken, New Jersey, 2007.

    “Continuous Navigation: Combining GPS with Sensor-based Dead Reckoning by G. zur Bonsen, D. Ammann, M. Ammann, E. Favey, and P. Flammant in GPS World, Vol. 16, No. 4, April 2005, pp. 47–54.

    “Inertial Navigation and GPS” by M.B. May in GPS World, Vol. 4, No. 9, September 1993, pp. 56–66.

    ◾ Kalman Filtering

    Kalman Filtering: Theory and Practice Using MATLAB, 2nd ed., by M.S. Grewal and A.P. Andrews, published by John Wiley & Sons Inc., New York, 2001.

    “The Kalman Filter: Navigation’s Integration Workhorse” by L.J. Levy, in GPS World, Vol. 8, No. 9, September, 1997, pp. 65–71.

    Applied Optimal Estimation by the Technical Staff, Analytic Sciences Corp., ed. A. Gelb, published by The MIT Press, Cambridge, Massachusetts, 1974.

    ◾ Parallel Cascade Identification

    “Simulation of Aircraft Pilot Flight Controls Using Nonlinear System Identification” by J.M. Eklund and M.J. Korenberg in Simulation, Vol. 75, No. 2, August 2000, pp.72–81, doi: 10.1177/003754970007500201.

    “Parallel Cascade Identification and Kernel Estimation for Nonlinear Systems” by M.J. Korenberg in Annals of Biomedical Engineering, Vol. 19, 1991, pp. 429–455, doi: 10.1007/ BF02584319.

    “Statistical Identification of Parallel Cascades of Linear and Nonlinear Systems” by M.J. Korenberg in Proceedings of the Sixth International Federation of Automatic Control Symposium on Identification and System Parameter Estimation, Washington, D.C., June 7–11, 1982, Vol. 1, pp. 580–585.

    ◾ On-Board Diagnostics

    “Low-cost PND Dead Reckoning using Automotive Diagnostic Links” by J.L. Wilson in Proceedings of ION GNSS 2007, the 20th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, September 25–28, 2007, pp. 2066–2074.

  • Innovation: The Right Attitude

    Innovation: The Right Attitude

    Experimenting with GPS on Board High-Altitude Balloons

    By Peter J. Buist, Sandra Verhagen, Tatsuaki Hashimoto, Shujiro Sawai, Shin-Ichiro Sakai, Nobutaka Bando, and Shigehito Shimizu

    In this month’s column, we look at how a team of Dutch and Japanese researchers is using GPS to determine the attitude of a payload launched from a high-altitude balloon.

    GPS World photo
    INNOVATION INSIGHTS by Richard Langley

    IT IS NOT WIDELY RECOGNIZED that relative or differential positioning using GNSS carrier-phase measurements is an interferometric technique. In interferometry, the difference in the phase of an electromagnetic wave at two locations is precisely measured as a function of time. The phase differences depend, amongst other factors, on the length and orientation of the baseline connecting the two locations. The classic demonstration of interferometry, showing that light could be interpreted as a wave phenomenon, was the 1803 double-slit experiment of the English polymath, Thomas Young.  Many of us recreated the experiment in high school or university physics classes. A collimated beam of light is shone through two small holes or narrow slits in a barrier placed between the light source and a screen. Alternating light and dark bands are seen on the screen. The bands are called interference fringes and result from the waves emanating from the two slits constructively and destructively interfering with each other. The colors seen on the surface of an audio CD, the colors of soap film, and those of peacock feathers and the wings of the Morpho butterfly are all examples of interference.

    Interference fringes also reveal information about the source of the waves. In 1920, the American Nobel-prize-winning physicist, Albert Michelson, used an interferometer attached to a large telescope to measure the diameter of the star Betelgeuse. Radio astronomers extended the concept to radio wavelengths, using two antennas connected to a receiver by cables or a microwave link. Such radio interferometers were used to study the structure of various radio sources including the sun. Using atomic frequency standards and magnetic tape recording, astronomers were able to sever the real-time links between the antennas, giving birth to very long baseline interferometry (VLBI) in 1967. The astronomers used VLBI to study extremely compact radio sources such as the enigmatic quasars. But geodesists realized that high resolution VLBI could also be used to determine — very precisely — the components of the baseline connecting the antennas, even if they were on separate continents.

    That early work in geodetic VLBI led to the concept developed by Charles Counselman III and others at the Massachusetts Institute of Technology in the late 1970s of recording the carrier phase of GPS signals with two separate receivers and then differencing the phases to create an observable from which the components of the baseline connecting the receivers’ antennas could be determined. This has become the standard high-precision GPS surveying technique. Later, others took the concept and applied it to short baselines on a moving platform allowing the attitude of the platform to be determined.

    In this month’s column, we look at how a team of Dutch and Japanese researchers is using GPS to determine the attitude of a payload launched from a high-altitude balloon.

    The Japan Aerospace Exploration Agency (JAXA) is developing a system to provide a high-quality, long duration microgravity environment using a capsule that can be released from a high-altitude balloon. Since 1981, an average of 100 million dollars is spent every year on microgravity research by space agencies in the United States, Europe, and Japan. There are many ways to achieve microgravity conditions such as (in order of experiment duration) drop towers, parabolic flights, balloon drops, sounding rockets, the Space Shuttle (unfortunately, no longer), recoverable satellites, and the International Space Station. The order of those options is also approximately the order of increasing experiment cost, with the exception of the balloon drop. Besides being cost-efficient, a balloon-based system has the advantage that no large acceleration is required before the experiment can be performed, which could be important for any delicate equipment that is carried aloft.

    In this article, we will describe JAXA’s Balloon-based Operation Vehicle (BOV) and the experiments carried out in cooperation with Delft University of Technology (DUT) using GPS on the gondola of the balloon in 2008 (single baseline estimation) and 2009 (full attitude determination and relative positioning). The attitude calculated using observations from the onboard GPS receiver during the 2009 experiment is compared with that from sun and geomagnetic sensors as well as that provided by the GPS receiver itself.

    Nowadays, GNSS is used for absolute and relative positioning of aircraft and spacecraft as well as determination of their attitude. What these applications have in common is that, in general, the orientation of the platform is changing relatively slowly and, to a large extent, predictably. Here, we will discuss a balloon-based application where the orientation of the platform, at times, varies very dynamically and unpredictably.

    Balloon Experiments

    Scientific balloons have been launched in Japan by the Institute of Space and Astronautical Science (ISAS), now a division of JAXA, since 1965, and it holds the world record for the highest altitude reached by a balloon — 53 kilometers. Recently, balloon launches have taken place from the Multipurpose Aviation Park (MAP) in Taiki on the Japanese island of Hokkaido. The balloons are launched using a so-called sliding launcher. The sliding launcher and the hanger at MAP are shown in FIGURE 1.

    Balloon-Based Operation Vehicle. As previously mentioned, JAXA’s BOV has been designed for microgravity research. The scenario of a microgravity experiment is illustrated in FIGURE 2. The vehicle is launched with a balloon, which carries it to an altitude of more than 40 kilometers, where it is released.

    Untitled-2 Source: Richard Langley
    Figure 2. Microgravity experiment procedure.

    After separation, the BOV is in free fall until the parachute is released so that the vehicle can make a controlled landing in the sea. The BOV is recovered by helicopter and can be reused. The capsule has a double-shell drag-free structure and it is controlled so as not to collide with the inner shell. The flight capsule, shown hanging at the sliding launcher in Figure 1, consists of a capsule body (the outer shell), an experiment module (the inner shell), and a propulsion system. The inner capsule shown in FIGURE 3 is kept in free-falling condition after release of the BOV from the balloon, and no disturbance force acts on this shell and the microgravity experiment it contains.

    Figure 3 BOV Overview Source: Richard Langley
    Figure 3. Balloon-based Operation Vehicle overview.

    The outer shell has a rocket shape to reduce aerodynamic disturbances. The distance between the outer and inner shells is measured using four laser range sensors. Besides the attitude of the BOV, the propulsion system controls the outer shell so that it does not collide with the inner s
    hell. The propulsion system uses 16 dry-air gas-jet thrusters of 60 newtons, each controlling it not only in the vertical direction but also in the horizontal direction to compensate disturbances from, for example, wind.

    Flight experiments with the BOV were carried out in 2006 (BOV1) and in 2007 (BOV2), when a fine microgravity environment was established successfully for more than 7 and 30 seconds, respectively.

    Attitude Determination. Balloon experiments are performed for a large number of applications, some of which require attitude control. Observations from balloon-based telescopes are an example of an application in which stratospheric balloons have to carry payloads of hundreds of kilograms to an altitude of more than 30 kilometers to be reasonably free of atmospheric disturbances. In this application, the typical requirement for the control of the azimuth angle of the platform is to within 0.1 degrees.

    JAXA is developing the Attitude Determination Package (ADP, see TABLE 1) for a future version of the BOV, which contains Sun Aspect Sensors (SAS), the Geomagnetic Aspect Sensor (GAS), an inclinometer, and a gyroscope. Each SAS determines the attitude with a resolution of one degree around one axis and the ADP has four of these sensors pointing in different directions. Inherently, this type of sensor can only provide attitude information if the sun is within the field of view of the sensor. The GAS also determines one-axis attitude. The resolution of magnetic flux density measured by the GAS and applied to obtain an attitude estimate is 50 parts per million. This results in an attitude determination accuracy of the GAS of 1.5 degrees with dynamic bias compensation. The inclinometer determines two-axis attitude with a resolution of 0.2 degrees.

    Table1 Source: Richard Langley
    Table 1. Sensor specifications.

    Background GPS Experiment. DUT is involved in a precise GPS-based relative positioning and attitude determination experiment onboard the BOV and the gondola of the balloon. Not only is the BOV a challenging environment, but so is the gondola itself, because of the rather rapidly varying attitude (due to wind and — especially at takeoff and separation — rotation) and the high altitude. For a GPS experiment, the altitude of around 40 kilometers is interesting as not many experiments have been performed at this height, which is higher than the altitude reachable by most aircraft but below the low earth orbits for spacecraft. An altitude of about 40 kilometers is a harsh environment for electrical devices because the pressure is about 1/1000 of an atmosphere and the temperature ranges from –60 to 0 degrees Celsius. Furthermore, the antennas are placed under the balloon, which affects the received GPS signals. Later on, we will describe in detail two experiments performed in 2008 and 2009, respectively.

    The GPS receivers on the first flight in 2008 were a navigation-type receiver, not especially adapted for such an experiment. The data was collected on a single baseline with two dual-frequency receivers. The receivers were controlled by, and the data stored on, an ARM Linux board using an RS-232 serial connection.

    For the second flight in 2009, we used a multi-antenna receiver, for which the Coordinating Committee for Multilateral Export Controls altitude restriction was explicitly removed. This receiver has three RF inputs that can be connected to three antennas, so the observations from the three antennas are time-synchronized by a common clock. The receiver has the option to store observations internally, which simplified the control of the GPS experiment. We used three antennas: one L1/L2 antenna as the main antenna and two L1 antennas as auxiliary antennas.

    Theory of Attitude Determination

    In this section, we will provide background information on the models applied in our GPS experiment. More details can be found in the publications listed in Further Reading.

    Standard LAMBDA. Most GNSS receivers make use of two types of observations: pseudorange (code) and carrier phase. The pseudorange observations typically have a precision of decimeters, whereas carrier-phase observations have precisions up to the millimeter level.

    Carrier-phase observations are affected, however, by an unknown number of integer-cycle ambiguities, which have to be resolved before we can exploit the higher precision of these observations. The observation equations for the double-difference (between satellites and between antennas/receivers) can be written for a single baseline as a system of linearized observation equations:
    

    Eq-1 Source: Richard Langley   (1)

    where E(y) is the expected value and D(y) is the dispersion of y. The vector of observed-minus-computed double-difference carrier-phase and code observations is given by y; z is the vector of unknown ambiguities expressed in cycles rather than distance units to maintain their integer character; b is the baseline vector, which is unknown for relative navigation applications but for which the length in attitude determination is generally known; A is a design matrix that links the data vector to the vector z; and B is the geometry matrix containing normalized line-of-sight vectors. The variance-covariance matrix of y is represented by the positive definite matrix Qyy, which is assumed to be known.

    The least-squares solution of the linear system of observation equations as introduced in Equation (1) is obtained using Eq-2 Source: Richard Langley  from:

    Eq-2b Source: Richard Langley  .  (2)

    The integer solution of this system can be obtained by applying the standard Least-Squares Ambiguity Decorrelation Adjustment (LAMBDA) method.

    Constrained LAMBDA. In applications for which some of the baseline lengths are known and constant, for example GNSS-based attitude determination, we can exploit the so-called baseline-constrained model. Then, the baseline-constrained integer ambiguity resolution can make use of the standard GNSS model by adding the length constraint of the baseline, ||b|| = Eq-l, where Eq-l is known. The least-squares criterion for this problem reads:

    Eq-3 Source: Richard Langley  .(3)

    The solution can be obtained with the baseline-constrained (or C-)LAMBDA method, which is described in referred literature listed in Further Reading. Later on, we will refer to the attitude calculated by this approach simply as C-LAMBDA.

    For platforms with more than one baseline, the C-LAMBDA method can be applied to each baseline individually, and the full attitude can be determined using those individual baseline solutions. For completeness, we also mention a recently developed solution of this problem, called the multivariate-constrained (MC-) LAMBDA, which integrally accounts for both the integer and attitude matrix. Both approaches are applied in the analyses of the BOV data.

    Onboard Attitude Determination. In this article, we also use the onboard estimate of the attitude as provided by the multi-antenna receiver. The method applied in the receiver is based on a Kalman filter and the ambiguities are resolved by the standard LAMBDA method. The baseline length, if the information is provided to the receiver a priori, is used to validate the results. For baseline lengths of about 1 meter, the receiver’s pitch and roll accuracy is about 0.60 degrees, and heading about 0.30 degrees according to the receiver manual. We will refer to the attitude as provided by the receiver as KF.

    Flight Experiments

    In this section, we will discuss our analyses of the GPS data from two of the BOV experiments.

    Gondola Experimental Flight 2008. In September 2008, we performed a test of the ADP for a future version of the BOV and a GPS system containing two navigation-grade GPS receivers. The goal of the experiment was to confirm nominal performance in the real environment of the ADP sensors and GPS receivers on the gondola; therefore, the BOV was not launched. The data from the single baseline was used to determine the pointing direction of the gondola, an application referred to as the GNSS compass. The receivers and the controller were stored in an airtight container (see FIGURE 4) and the antennas were sealed in waterproof bags. The location of the two GPS antennas on the gondola is indicated in Figure 4. The baseline length was 1.95 meters. Both receivers used their own individual clocks, so observations were not synchronized. The trajectory (altitude) of this flight is shown in the right-hand side of Figure 4, with the longitude and latitude shown in FIGURE 5. This is a typical flight profile for our application. The flight takes about three hours and reaches an altitude of more than 40 kilometers.

    Fig4b Source: Richard Langley
    Figure 4B. Single baseline experiment performed in September 2008, the flight trajectory (altitude).

    First, the balloon makes use of the wind direction in the lower layers of the atmosphere, which brings it eastwards. During this part of the flight, the balloon is kept at a maximum altitude of about 12 kilometers. After about 30 minutes, the altitude is increased to make use of a different wind direction that carries the balloon back in the westerly direction toward the launch base in order to ease the recovery of the capsule and/or the gondola.

    At the end of the flight, there is a parachute-guided fall over 40 kilometers to sea level, for both the gondola and the BOV (if it is launched), which takes about 30 minutes. In this experiment, we could confirm the nominal operation of some of the sensors and reception of the GPS signals on the gondola under the large balloon.

    Gondola Experimental Flight 2009. In May 2009, the third flight of the BOV was performed. The three GPS receiver antennas and the other attitude sensors were placed on an alignment frame for stiffness, which was then attached to the gondola. Furthermore, we used a ground station to demonstrate the combination of GPS-based attitude determination and relative positioning between the platform and the ground station. As the motion of the system is rather unpredictable, we used a kinematic approach for both attitude determination and relative positioning.

    Preflight static test: Before the flight, we did a ground test using the actual antenna frame of the gondola (see FIGURE 6). The roll, pitch, and heading angles for this static test are shown on the right-hand side of this figure. Due to the geometry of the baselines, the heading angle is more accurate. For this static test, we can calculate the standard deviation of the three angles to confirm the accuracy achievable for the flight test. These results are summarized in TABLE 2. For the baselines with a length of about 1.4 meters, we achieved an accuracy of about 0.25 degrees for the roll and pitch angles and 0.1 degrees for heading, which is as expected from the lengths and geometry of the baselines. Using single-epoch data, we could resolve the ambiguities correctly for more than 99 percent of the epochs (see TABLE 3). Also, the standard deviation of the receiver’s Kalman-filter-based attitude estimate (KF) is included in the table. The accuracy is, after convergence of the filter, similar to our C-LAMBDA result, although the applied method is very different. The Kalman filter takes about 10 seconds to converge for this static experiment, whereas the C-LAMBDA method provides this accuracy from the very first epoch. For completeness, the instantaneous success rate of the standard LAMBDA and MC-LAMBDA methods are also included in Table 3.

    Figure 6 C-LAMBDA based attitude estimates on right Source: Richard Langley
    Figure 6. Static experiment: C-LAMBDA-based attitude estimates.
    Table2 Source: Richard Langley
    Table 2. Standard deviation of attitude angles for static test.
    Table3 Source: Richard Langley
    Table 3. Single-epoch, overall success rate for baseline 1-2 (static experiment).

    Gondola nominal flight: Next, we applied the same GPS configuration on the gondola. An important difference with respect to the static field experiment is that the antennas were now placed under the balloon and inside waterproof bags (see the picture on the left-hand side of FIGURE 7). The right-hand side of Figure 7 shows the flight trajectory (altitude) of the experiment. At 21:05 UTC (07:05 Japan Standard Time), the balloon was released from the sliding launcher (Figure 1). In 2.5 hours, the balloon reached an altitude of more than 41 kilometers from which the BOV was dropped. At 23:55, the BOV was released from the Gondola, and at 23:59 the gondola was separated from the balloon. After the release of the BOV, the balloon and gondola ascended more than 2 kilometers because of the reduced mass of the system. For this flight, the attitude determination package and the GPS system were installed on the gondola to confirm the nominal performance of all the sensors.

    Figure 8 sensor configuration Source: Richard Langley
    Figure 7A. Full attitude experiment performed in May 2009, sensor configuration.
    Figure 8 flight trajectory (altitude ) on rightSource: Richard Langley
    Figure 7B. Full attitude experiment performed in May 2009, flight trajectory (altitude).

    Using the new GPS receiver with three antennas, we are able to calculate the full attitude of the gondola. The roll and pitch estimates, from both C-LAMBDA and KF, are shown in FIGURE 8. The heading angle from the GPS-based C-LAMBDA and KF, and that from the GAS and SAS sensors are shown in FIGURE 9. As explained in a previous section, the four SAS sensors will only output an attitude estimate if the sun is in the field of view of a sensor. Therefore we can distinguish four bands in the heading estimate of the SAS, corresponding to the individual sensors (indicated in Figure 7 as SAS1 to SAS4).

    Figure 9 GPS results for roll (left) angels during nominal fligh Source: Richard Langley
    Figure 8A. GPS results for roll angles during nominal flight.
    Figure 9 GPS results for pitch (right) angels during nominal fl Source: Richard Langley
    Figure 8B. GPS results for pitch angles during nominal flight.
    Figure 10 GPS (left)
    Figure 9A. GPS results for heading angle during nominal flight.
    Figure 10 GAS and SAS (right) Source: Richard Langley
    Figure 9B. GAS and SAS results for heading angle during nominal flight.

    The number of locked GPS satellites at the main antenna is shown on the right-hand side of Figure 7. Before takeoff, we saw that the number of locked channels varies rapidly due to obstructions, but after takeoff the number is rather constant until the BOV is separated from the gondola. Before takeoff, the GPS observations are affected by the obstruction of the sliding launcher and therefore ambiguity resolution is only possible on the second baseline (see Figure 8). Also, the GPS receiver itself does not provide an attitude estimation during this phase of the experiment. During takeoff, we see large variations in orientation of the gondola (up to 20 degrees (±10 degrees) for both roll and pitch), which can be estimated well by both C-LAMBDA and KF. Again, the Kalman filter takes a few epochs to converge (in this case, 15 seconds from takeoff), whereas the C-LAMBDA method provides an accurate solution from the very first epoch. After takeoff, the attitude of the gondola stabilizes and the C-LAMBDA and KF attitude estimates are very similar.

    We investigated the difference between the attitude estimation from the different sensors during nominal flight. The mean and standard deviations of the differences are shown in TABLE 4. If we compare the C-LAMBDA and KF attitudes, we observe biases for all angles. This is something we have to investigate further, but the most likely cause for this bias is the time delay of the Kalman filter in response to changes in attitude, as we observed in the static experiment in the form of convergence time.

    Table4 Source: Richard Langley
    Table 4. Attitude differences (offset/standard deviation) for flight test of 2009.

    The standard deviation for the difference in the estimates of roll, pitch, and heading is as expected. For the comparison with the other sensors, we use the C-LAMBDA attitude as the reference. Between C-LAMBDA and GAS/SAS, we observe a bias, most likely due to minor misalignment issues between the sensors. The standard deviations in Table 4 are in line with expectation based on the sensor specifications. During this part of the flight, we achieved a single-epoch, single-frequency empirical overall success rate for ambiguity resolution on the two baselines of 95.09 percent. As a reference, we also include in TABLE 5 the success rate for standard LAMBDA using observations from a single epoch. If we make use of the MC-LAMBDA method, the success rate is increased to 99.88 percent as shown in the table. The success rate is higher as the integrated model for all the baselines is stronger.

    Table 5. Single-epoch, overall success rate for baseline 1-2 (flight experiment). Source: Richard Langley
    Table 5. Single-epoch, overall success rate for baseline 1-2 (flight experiment).

    Gondola flight after BOV separation: After the separation of the BOV from the gondola, the gondola starts to ascend and sway. FIGURE 10 contains roll and pitch estimates for this part of the flight until the gondola separation. In the figure, we see large variations in the orientation of the gondola (up to 40 (±20) degrees for roll and 20 (± 10) degrees for pitch). It is interesting that after BOV separation, during the large maneuvers of the gondola caused by the separation, both KF and C-LAMBDA estimates are available but to a certain extent are different. Table 4 also contains standard deviations and biases between C-LAMBDA and KF for this part of the flight.

    Figure 11 GPS results for roll (left) Source: Richard Langley
    Figure 10A. GPS results for roll angles during nominal flight.
    Fig10b Source: Richard Langley
    Figure 10B. GPS results for pitch angles during nominal flight.

    We conclude that the differences (standard deviation but also bias) between C-LAMBDA and KF — both for roll and pitch — are increased compared to the nominal part of the flight. This confirms our expectation that the Kalman-filter-based result lags behind the true attitude in dynamic situations, whereas the C-LAMBDA result based on single-epoch data should be able to provide the same accurate estimate as during the other phases of the flight.

    Future Work

    For the final phase of the experiment program, we would like to collect multi-baseline data from a number of vehicles. The preferred option for the experiment is three antennas (two independent baselines) on the BOV, and two antennas (one baseline) on the gondola. Furthermore, similar to our 2009 experiment, a number of antennas at a reference station could be used. The goal of the final phase of the program is to collect data for offline relative positioning and attitude determination, though real-time emulation, between a number of vehicles that form a network.

    Acknowledgments

    Peter Buist thanks Professor Peter Teunissen for support with the theory behind ambiguity resolution and, including Gabriele Giorgi, for the pleasant cooperation during our research. The MicroNed-MISAT framework is kindly thanked for their support. The research of Sandra Verhagen is supported by the Dutch Technology Foundation STW, the Applied Science Division of The Netherlands Organisation for Scientific Research (NWO), and the Technology Program of the Ministry of Economic Affairs. This article is based on the paper “GPS Experiment on the Balloon-based Operation Vehicle” presented at the Institute of Electrical and Electronics Engineers / Institute of Navigation Position Location and Navigation Symposium 2010, held in Indians Wells, California, May 6–8, 2010, where it received a best-paper-in-track award.

    Manufacturers

    The Attitude Determination Package’s Sun Aspect Sensor is based on photodiodes manufactured by Hamamatsu Photonics K.K.; the Geomagnetic Aspect Sensor is based on magnetometers manufactured by Bartington Intruments Ltd.; the inclinometer is based on a module manufactured by Measurement Specialties; and the gyro is manufactured by Silicon Sensing Systems Japan, Ltd. For the 2009 experiment, we used a Septentrio N.V. PolaRx2@ multi-antenna receiver with S67-1575-96 and S67-1575-46 antennas from Sensor Systems Inc. Details on the receivers and antennas used for the 2008 experiment are not publicly available. A Trimble Navigation Ltd. R7 receiver and two NovAtel Inc. OEMV receivers were used at the reference ground station. The ARM-Linux logging computer is an Armadillo PC/104 manufactured by Atmark Techno, Inc.

    Peter J. Buist is a researcher at Delft University of Technology in Delft, The Netherlands. Before rejoining DUT in 2006, he developed GPS receivers for the SERVIS-1, USERS, ALOS, and other satellites and the H2A rocket, and subsystems for QZSS in the Japanese space industry.

    Sandra Verhagen is an assistant professor at Delft University of Technology in Delft, The Netherlands. Together with Peter Buist, she is working on the Australian Space Research Program GARADA project on synthetic aperture radar formation flying.

    Tatsuaki Hashimoto received his Ph.D. in electrical engineering from the University of Tokyo in 1990. He is a professor of the Institute of Space and Astronautical Science (ISAS), Japan Aerospace Exploration Agency (JAXA).

    Shujiro Sawai received his Ph.D. in engineering from the University
    of Tokyo in 1994. He is an associate professor at ISAS/JAXA.

    Shin-Ichiro Sakai received his Ph.D. degree from the University of Tokyo in 2000. He joined ISAS/JAXA in 2001 and became associate professor in 2005.

    Nobutaka Bando received a Ph.D. in electrical engineering from the University of Tokyo in 2005. He is an assistant professor at ISAS/JAXA.

    Shigehito Shimizu received a master’s degree in engineering from Tohoku University in Sendai, Japan, in 2007. He is an engineer in the Navigation, Guidance and Control Group at JAXA.

    FURTHER READING

    • Authors’ Proceedings Paper
    “GPS Experiment on the Balloon-based Operation Vehicle” by P.J. Buist, S. Verhagen, T. Hashimoto, S. Sawai, S-I. Sakai, N. Bando, and S. Shimizu in Proceedings of PLANS 2010, IEEE/ION Position Location and Navigation Symposium, Indian Wells, California, May 4–6, 2010, pp. 1287–1294, doi: 10.1109/PLANS.2010.5507346.

    • Balloon Applications
    “Development of Vehicle for Balloon-Based Microgravity Experiment and Its Flight Results” by S. Sawai, T. Hashimoto, S. Sakai, N. Bando, H. Kobayashi, K. Fujita, T. Yoshimitsu, T. Ishikawa, Y. Inatomi, H. Fuke, Y. Kamata, S. Hoshino, K. Tajima, S. Kadooka, S. Uehara, T. Kojima, S. Ueno, K. Miyaji, N. Tsuboi, K. Hiraki, K. Suzuki, and K. M. T. Nakata in Journal of the Japan Society for Aeronautical and Space Sciences, Vol. 56, No. 654, 2008, pp. 339–346, doi: 10.2322/jjsass.56.339.

    “Development of the Highest Altitude Balloon” by T. Yamagami, Y. Saito, Y. Matsuzaka, M. Namiki, M. Toriumi, R. Yokota, H. Hirosawa, and K. Matsushima in Advances in Space Research, Vol. 33, No. 10, 2004, pp. 1653–1659, doi: 10.1016/j.asr.2003.09.047.

    • Attitude Determination
    “Testing of a New Single-Frequency GNSS Carrier-Phase Attitude Determination Method: Land, Ship and Aircraft Experiments” by P.J.G. Teunissen, G. Giorgi, and P.J. Buist in GPS Solutions, Vol. 15, No. 1, 2011, pp. 15–28, doi: 10.1007/s10291-010-0164-x, 2010.

    “Attitude Determination Methods Used in the PolarRx2@ Multi-antenna GPS Receiver” by L.V. Kuylen, F. Boon, and A. Simsky in Proceedings of ION GNSS 2005, the 18th International Technical Meeting of the Satellite Division of The Institute of Navigation, Long Beach, California, September 13–16, 2005, pp. 125–135.

    “Design of Multi-sensor Attitude Determination System for Balloon-based Operation Vehicle” by S. Shimizu, P.J. Buist, N. Bando, S. Sakai, S. Sawai, and T. Hashimoto, presented at the 27th ISTS International Symposium on Space Technology and Science, Tsukuba, Japan, July 5–12, 2009.

    “Development of the Integrated Navigation Unit; Combining a GPS Receiver with Star Sensor Measurements” by P.J. Buist, S. Kumagai, T. Ito, K. Hama, and K. Mitani in Space Activities and Cooperation Contributing to All Pacific Basin Countries, the Proceedings of the 10th International Conference of Pacific Basin Societies (ISCOPS), Tokyo, Japan, December 10–12, 2003, Advances in the Astronautical Sciences, Vol. 117, 2004, pp. 357–378.

    “Solving Your Attitude Problem: Basic Direction Sensing with GPS” by A. Caporali in GPS World, Vol. 12, No. 3, March 2001, pp. 44–50.

    • Ambiguity Estimation
    “Instantaneous Ambiguity Resolution in GNSS-based Attitude Determination Applications: the MC-LAMBDA Method” by G. Giorgi, P.J.G. Teunissen, S. Verhagen, and P.J. Buist in Journal of Guidance, Control and Dynamics, accepted for publication, April 2011.

    “Integer Least Squares Theory for the GNSS Compass” by P.J.G. Teunissen in Journal of Geodesy, Vol. 84, No. 7, 2010, pp. 433–447, doi: 10.1007/s00190-010-0380-8.

    “The Baseline Constrained LAMBDA Method for Single Epoch, Single Frequency Attitude Determination Applications” by P.J. Buist in Proceedings of ION GPS 2007, the 20th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, September 25–28, 2007, pp. 2962–2973.

    “The LAMBDA Method for the GNSS Compass” by P.J.G. Teunissen in Artificial Satellites, Vol. 41, No. 3, 2006, pp. 89–103, doi: 10.2478/v10018-007-0009-1.

    “Fixing the Ambiguities: Are You Sure They’re Right?” by P. Joosten and C. Tiberius in GPS World, Vol. 11, No. 5, May 2000, pp. 46–51.

    “The Least-Squares Ambiguity Decorrelation Adjustment: a Method for Fast GPS Integer Ambiguity Estimation” by P.J.G. Teunissen in Journal of Geodesy, Vol. 70, No. 1–2, 1995, pp. 65–82, doi: 10.1007/BF00863419.

    • Relative Positioning
    “A Vectorial Bootstrapping Approach for Integrated GNSS-based Relative Positioning and Attitude Determination of Spacecraft” by P.J. Buist, P.J.G. Teunissen, G. Giorgi, and S. Verhagen in Acta Astronautica, Vol. 68, No. 7-8, 2011, pp. 1113–1125, doi: 10.1016/j.actaastro.2010.09.027.

  • Innovation: Multipath Minimization Method

    Innovation: Multipath Minimization Method

    Mitigation Through Adaptive Filtering for Machine Automation Applications

    By Luis Serrano, Don Kim, and Richard B. Langley

    Multipath is real and omnipresent, a detriment when GPS is used for positioning, navigation, and timing. The authors look at a technique to reduce multipath by using a pair of antennas on a moving vehicle together with a sophisticated mathematical model. This reduces the level of multipath on carrier-phase observations and thereby improves the accuracy of the vehicle’s position.

    GPS World photo
    INNOVATION INSIGHTS by Richard Langley

    “OUT, DAMNED MULTIPATH! OUT, I SAY!” Many a GPS user has wished for their positioning results to be free of the effect of multipath. And unlike Lady Macbeth’s imaginary blood spot, multipath is real and omnipresent. Although it may be considered beneficial when GPS is used as a remote sensing tool, it is a detriment when GPS is used for positioning, navigation, and timing — reducing the achievable accuracy of results.

    Clearly, the best way to reduce the effects of multipath is to try avoiding it in the first place by siting the receiver’s antenna as low as possible and far away from potential reflectors. But that’s not always feasible. The next best approach is to reduce the level of the multipath signal entering the receiver by attenuating it with a suitably designed antenna. A large metallic ground plane placed beneath an antenna will modify the shape of the antenna’s reception pattern giving it reduced sensitivity to signals arriving at low elevation angles and from below the antenna’s horizon. So-called choke-ring antennas also significantly attenuate multipath signals. And microwave-absorbing materials appropriately placed in an antenna’s vicinity can also be beneficial.

    Multipath can also be mitigated by special receiver correlator designs. These designs target the effect of multipath on code-phase measurements and the resulting pseudorange observations. Several different proprietary implementations in commercial receivers significantly reduce the level of multipath in the pseudoranges and hence in pseudorange-based position and time estimates. Some degree of multipath attenuation can be had by using the low-noise carrier-phase measurements to smooth the pseudoranges before they are processed. The effect of multipath on carrier phases is much smaller than that on pseudoranges. In fact, it is limited to only one-quarter of the carrier wavelength when the reflected signal’s amplitude is less than that of the direct signal. This means that at the GPS L1 frequency, the multipath contamination in a carrier-phase measurement is at most about 5 centimeters. Nevertheless, this is still unacceptably large for some high-accuracy applications.

    At a static site, with an unchanging multipath environment, the signal reflection geometry repeats day to day and the effect of multipath can be reduced by sidereal filtering or “stacking” of coordinate or carrier-phase-residual time series. However, this approach is not viable for scenarios where the receiver and antenna are moving such as in machine control applications. Here an alternative approach is needed.

    In this month’s column, I am joined by two of my UNB colleagues as we look at a technique that uses a pair of antennas on a moving vehicle together with a sophisticated mathematical model, to reduce the level of multipath on carrier-phase observations and thereby improve the accuracy of the vehicle’s position.

    Real-time-kinematic (RTK) GNSS-based machine automation systems are starting to appear in the construction and mining industries for the guidance of dozers, motor graders, excavators, and scrapers and in precision agriculture for the guidance of tractors and harvesters. Not only is the precise and accurate position of the vehicle needed but its attitude is frequently required as well.

    Previous work in GNSS-based attitude systems, using short baselines (less than a couple of meters) between three or four antennas, has provided results with high accuracies, most of the time to the sub-degree level in the attitude angles. If the relative position of these multiple antennas can be determined with real-time centimeter-level accuracy using the carrier-phase observables (thus in RTK-mode), the three attitude parameters (the heading, pitch, and roll angles) of the platform can be estimated.

    However, with only two GNSS antennas it is still possible to determine yaw and pitch angles, which is sufficient for some applications in precision agriculture and construction. Depending on the placement of the antennas on the platform body, the determination of these two angles can be quite robust and efficient.

    Nevertheless, even a small separation between the antennas results in different and decorrelated phase-multipath errors, which are not removed by simply differencing measurements between the antennas.

    The mitigation of carrier-phase multipath in real time remains, to a large extent, very limited (unlike the mitigation of code multipath through receiver improvements) and it is commonly considered the major source of error in GNSS-RTK applications. This is due to the very nature of multipath spectra, which depends mainly on the location of the antenna and the characteristics of the reflector(s) in its vicinity. Any change in this binomial (antenna/reflectors), regardless of how small it is, will cause an unknown multipath effect.

    Using typical choke-ring antennas to reduce multipath is typically not practical (not to mention cost prohibitive) when employing multiple antennas on dynamic platforms. Extended flat ground planes are also impractical. Furthermore, such antenna configurations typically only reduce the effects of low angle reflections and those coming from below the antenna horizon.

    One promising approach to mitigating the effects of carrier-phase multipath is to filter the raw measurements provided by the receiver. But, unlike the scenario at a fixed site, the multipath and its effects are not repeatable. In machine automation applications, the machinery is expected to perform complex and unpredictable maneuvers; therefore the removal of carrier-phase multipath should rely on smart digital filtering techniques that adapt not only to the background multipath (coming mostly from the machine’s reflecting surfaces), but also to the changing multipath environment along the machine’s path.

    In this article, we describe how a typical GPS-based machine automation application using a dual-antenna system is used to calibrate, in a first step, and then remove carrier-phase multipath afterwards. The intricate dynamical relationship between the platform’s two “rover” antennas and the changing multipath from nearby reflectors is explored and modeled through several stochastic and dynamical models. These models have been implemented in an extended Kalman filter (EKF).

    MIMICS Strategy

    Any change in the relative position between a pair of GNSS antennas most likely will affect, at a small scale, the amplitude and polarization of the reflected signals sensed by the antennas (depending on their spacing). However, the phase will definitely change significantly along the ray trajectories of the plane waves passing through each of the antennas.

    This can be seen in the equation that describes the single-difference multipath between two close-by antennas (one called the “master” and the other the “slave”):

    Eq-1 Source: Richard Langley  (1)

    where the angle Eq-1a Source: Richard Langley is the relative multipath phase delay between the antennas and a nearby effective reflector (α0 is the multipath signal amplitude in the master and slave antennas, and is dependent on the reflector characteristics, reflection coefficient, and receiver tracking loop).

    As our study has the objective to mimic as much as possible the multipath effect from effective reflectors in kinematic scenarios with variable dynamics, we decided to name the strategy MIMICS, a slightly contrived abbreviation for “Multipath profile from between receIvers dynaMICS.”

    The MIMICS algorithm for a dual-antenna system is based on a specular reflector ray-tracing multipath model (see Figure 1).

    Fig1 Source: Richard Langley
    Figure 1. 3D ray-tracing modeling of phase multipath for a GNSS dual-antenna system. 0 designates the “master” antenna; 1, the “slave” antenna; Elev and Az, the elevation angle and the azimuth of the satellite, respectively. The other symbols are explained in the text.

    After a first step of data synchronization and data-snooping on the data provided by the two receiver antennas, the second step requires the calculation of an approximate position for both antennas, relaxed to a few meters using a standard code solution.

    A precise estimation of both antennas’ velocity and acceleration (in real time) is carried out using the carrier-phase observable. Not only should the antenna velocity and acceleration estimates be precisely determined (on the order of a few millimeters per second and a few millimeters per second squared, respectively) but they should also be immune to low-frequency multipath signatures. This is important in our approach, as we use the antennas’ multipath-free dynamic information to separate the multipath in the raw data.

    We will start from the basic equations used to derive the single-difference multipath observables.

    The observation equation for a single-difference between receivers, using a common external clock (oscillator), is given (in distance units) by:

    Eq-2 Source: Richard Langley (2)

    where m indicates the master antenna; s, the slave antenna; prn, the satellite number; Δ, the operator for single differencing between receivers; Φ, the carrier-phase observation; ρ, the slant range between the satellite and receiver antennas; N, the carrier-phase ambiguity; M, the multipath; and ε, the system noise.

    By sequentially differencing Equation (2) in time to remove the single-difference ambiguity from the observation equation, we obtain (as long as there is no loss of lock or cycle slips):

    Eq-3 Source: Richard Langley(3)

    where

    Eq-4 Source: Richard Langley(4)

    One of the key ideas in deriving the multipath observable from Equation (3) is to estimate Eq-4a Source: Richard Langley given by Equation (4). We will outline our approach in a later section.

    From Equation (3), at the second epoch, for example, we will have:

    EQ-5 Source: Richard Langley(5)

    If we continue this process up to epoch n, we will obtain an ensemble of differential multipath observations.

    If we then take the numerical summation of these, we will have

    Eq-6 Source: Richard Langley(6)

    Note that n samples of differential multipath observations are used in Equation (6). Therefore, we need n + 1 observations.

    Assume that we perform this process taking n = 1, then n = 2, and so on until we obtain r numerical summations of Equation (6) and then take a second numerical summation of them, we will end up with the following equation:

    Eq-7 Source: Richard Langley(7)

    where E is the expectation operator.

    Another key idea in our approach is associated with Equation (7). To isolate the initial epoch multipath, Eq-7a Source: Richard Langley, from the differential multipath observations, the first term on the right-hand side of Equation (7), Eq-7b Source: Richard Langley, should be removed.

    This can be accomplished by mechanical calibration and/or numerical randomization. To summarize the idea, we have to create random multipath physically (or numerically) at the initialization step. When the isolation of the initial multipath epoch is completed, we can recover multipath at every epoch using Equation (5).

    Digital Differentiators. We introduce digital differentiators in our approach to derive higher order range dynamics (that is, range rate, range-rate change, and so on) using the single-difference (between receivers connected to a common external oscillator) carrier-phase observations. These higher order range dynamics are used in Equation (4).

    There are important classes of finite-impulse-response differentiators, which are highly accurate at low to medium frequencies. In central-difference approximations, both the backward and the forward values of the function are used to approximate the current value of the derivative.

    Researchers have demonstrated that the coefficients of the maximally linear digital differentiator of order 2N + 1 are the same as the coefficients of the easily computed central-difference approximation of order N.

    Another advantage of this class is that within a certain maximum allowable ripple on the amplitude response of the resultant differentiator, its pass band can be dramatically increased. In our approach, this is something fundamental as the multipath in kinematic scenarios is conceptually treated as high-frequency correlated multipath, depending on the platform dynamics and the distance to the reflector(s).

    Adaptive Estimation. To derive single-difference multipath at the initial epoch, Source: Richard Langley, a numerical randomization (or mechanical calibration) of the single-difference multipath observations is performed in our approach. A time series of the single-difference multipath observations to be randomized is given as

    Eq-8 Source: Richard Langley(8)

    Then our goal is to achieve the following condition:

    Eq-9 Source: Richard Langley(9)

    It is obvious that the condition will only hold if multipath truly behaves as a stochastic or random process. The estimation of multipath in a kinematic scenario has to be understood as the estimation of time-correlated random errors. However, there is no straightforward way to find the correlation periods and model the errors.

    Our idea is to decorrelate the between-antenna relative multipath through the introduction of a pseudorandom motion. As one cannot completely rely only on a decorrelation through the platform calibration motion, one also has to do it through the mathematical “whitening” of the time series.

    Nevertheless, the ensemble of data depicted in the above formulation can be modeled as an oscillatory random process, for which second or higher order autoregressive (AR) models can provide more realistic modeling in kinematic scenarios. (An autoregressive process is simply another name for a linear difference equation model where the input or forcing function is white Gaussian noise.) We can estimate the parameters of this model in real time, in a block-by-block analysis using the familiar Yule-Walker equations. A whitening filter can then be formed from the estimation parameters.

    We obtain the AR coefficients using the autocorrelation coefficient vector of the random sequences. Since the order of the coefficient estimation depends on the multipath spectra (in turn dependent on the platform dynamics and reflector distance), MIMICS uses a cost function to estimate adaptively, in real time, the appropriate order. An order too low results in a poor whitener of the background colored noise, while an order too large might affect the embedded original signal that we are interested in detecting.

    The cost function uses the residual sum of squared error. The order estimate that gives the lowest error is the one chosen, and this task is done iteratively until it reaches a minimum threshold value. Once this stage is fulfilled, the multipath observable can be easily obtained.

    Testing

    The main test that we have performed so far (using a pair of high performance dual-frequency receivers fed by compact antennas and a rubidium frequency standard, all installed in a vehicle) was designed to evaluate the amount of data necessary to perform the decorrelation, and to determine if the system was observable (in terms of estimating, at every epoch, several multipath parameters from just two-antenna observations). Receiver data was collected and post-processed (so-called RTK-style processing) although, with sufficient computing power, data processing could take place in real, or near real, time.

    In a real-life scenario, the platform pseudorandom motions have the advantage that carrier-phase embedded dynamics are typically changing faster and in a three-dimensional manner (antennas sense different pitch and yaw angles). Thus a faster and more robust decorrelation is possible.

    One can see from the bottom picture in Figure 2 the façade of the building behaving as the effective reflector. The vehicle performed several motions, depicted in the bottom panel of Figure 3, always in the visible parking lot, hence the building constantly blocked the view to some satellites. We used only the L1 data from the receivers recorded at a rate of 10 Hz.

    In the bottom panel of Figure 3, one can also see the kind of motion performed by the platform. Accelerations, jerk, idling, and several stops were performed on purpose to see the resultant multipath spectra differences between the antennas. The reference station (using a receiver with capabilities similar to those in the vehicle) was located on a roof-top no more than 110 meters away from the vehicle antennas during the test. As such, most of the usual biases where removed from the solution in the differencing process and the only remaining bias can be attributed to multipath. The data from the reference receiver was only used to obtain the varying baseline with respect to the vehicle master antenna.

    In the top panel of Figure 3, one can see the geometric distance calculated from the integer-ambiguity-fixed solutions of both antenna/receiver combinations. Since the distance between the mounting points on the antenna-support bar was accurately measured before the test (84 centimeters), we had an easy way to evaluate the solution quality. The “outliers” seen in the figure come from code solutions because the building mentioned before blocked most of the satellites towards the southeast. As a result, many times fewer than five satellites were available.

    Fig3 Source: Richard Langley
    Figure 3. Correlation between vehicle dynamics (heading angle) and the multipath spectra.

    Looking at the first nine minutes of results in Figure 4, one can see that when the vehicle is still stationary, the multipath has a very clear quasi-sinusoidal behavior with a period of a few minutes. Also, one can see that it is zero-mean as expected (unlike code multipath). When the vehicle starts moving (at about the four-minute mark), the noise figure is amplified (depending on the platform velocity), but one can still see a mixture of low-frequency components coming from multipath (although with shorter periods).

    These results indicate, firstly, that regardless of the distance between two antennas, multipath will not be eliminated after differencing, unlike some other biases. Secondly, when the platform has multiple dynamics, multipath spectra will change accordingly starting from the low-frequency components (due to nearby reflectors) towards the high-frequency ones (including diffraction coming from the building edges and corners). As such, our approach to adaptively model multipath in real time as a quasi-random process makes sense.

    Figure 4. Position results from the kinematic test, showing the estimated distance between the two vehicle antennas (upper plot) and the distance between the master antenna and the reference antenna. Source: Richard Langley
    Figure 4. Position results from the kinematic test, showing the estimated distance between the two vehicle antennas (upper plot) and the distance between the master antenna and the reference antenna.

    Multipath Observables. The multipath observables are obtained through the MIMICS algorithm. It is quite flexible in terms of latency and filter order when it comes to deriving the observables. Basically, it is dependent on the platform dynamics and the amplitude of the residuals of the whitened time series (meaning that if they exceed a certain threshold, then the filtering order doesn’t fit the data).

    When comparing the observations delivered every half second for PRN 5 with the ones delivered every second, it is clear that the larger the interval between observations, the better we are able to recover the true biased sinusoidal behavior of multipath. However, in machine control, some applications require a very low latency. Therefore, there must be a compromise between the multipath observable accuracy and the rate at which it is generated.

    Multipath Parameter Estimation. Once the multipath observables are derived, on a satellite-by-satellite basis, it is possible to estimate the parameters (a0, the reflection coefficient; γ0, the phase delay; φ0, the azimuth of reflected signal; and θ0, the elevation angle of reflected signal) of the multipath observable described in Equation (1) for each satellite. As mentioned earlier, an EKF is used for the estimation procedure.

    When the platform experiences higher dynamics, such as rapid rotations, acceleration is no longer constant and jerk is present. Therefore, a Gauss-Markov model may be more suitable than other stochastic models, such as random walk, and can be implemented through a position-velocity-acceleration dynamic model.

    As an example, the results from the multipath parameter estimation are given for satellite PRN 5 in Figure 5. One can see that it takes roughly 40 seconds for the filter to converge. This is especially seen in the phase delay.

    Converted to meters, the multipath phase delay gives an approximate value of 10 meters, which is consistent with the distance from the moving platform to the dominant specular reflector (the building’s façade).

    Figure 5. PRN 5 multipath parameter estimation. Source: Richard Langley
    Figure 5. PRN 5 multipath parameter estimation.

    Multipath Mitigation. After going through all the MIMICS steps,
    from the initial data tracking and synchronization between the dual-antenna system up to the multipath parameter estimation for each continuously observed satellite, we can now generate the multipath corrections and thus correct each raw carrier-phase observation.

    One can see in Figure 6 three different plots from the solution domain depicting the original raw (multipath-contaminated) GPS-RTK baseline up-component (top), the estimated carrier-phase multipath signal (middle), and the difference between the two above time series; that is, the GPS-RTK multipath-ameliorated solution (bottom). A clear improvement is visible. In terms of numbers, and only considering the results “cleaned” from outliers and differential-code solutions (provided by the RTK post-processing software, when carrier-phase ambiguities cannot be fixed), the up-component root-mean-square value before was 2.5 centimeters, and after applying MIMICS it stood at 1.8 centimeters.

    Figure 6. MIMICS algorithm results for the vehicle baseline from the first 9 minutes of the test.
    Figure 6. MIMICS algorithm results for the vehicle baseline from the first 9 minutes of the test.

    Concluding Remarks

    Our novel strategy seems to work well in adaptively detecting and estimating multipath profiles in simulated real time (or near real time as there is a small latency to obtain multipath corrections from the MIMICS algorithm). The approach is designed to be applied in specular-rich and varying multipath environments, quite common at construction sites, harbors, airports, and other environments where GNSS-based heading systems are becoming standard. The equipment setup can be simplified, compared to that used in our test, if a single receiver with dual-antenna inputs is employed.

    Despite its success, there are some limitations to our approach. From the plots, it’s clear that not all multipath patterns were removed, even though the improvements are notable. Moreover, estimating multipath adaptively in real time can be a problem from a computational point of view when using high update rates. And when the platform is static and no previous calibration exists, the estimation of multipath parameters is impossible as the system is not observable. Nevertheless, the approach shows promise and real-world tests are in the planning stages.

    Acknowledgments

    The work described in this article was supported by the Natural Sciences and Engineering Research Council of Canada. The article is based on a paper given at the Institute of Electrical and Electronics Engineers / Institute of Navigation Position Location and Navigation Symposium 2010, held in Indian Wells, California, May 6–8, 2010.

    Manufacturers

    The test of the MIMICS approach used two NovAtel OEM4 receivers in the vehicle each fed by a separate NovAtel GPS-600 “pinweel” antenna on the roof. A Temex Time (now Spectratime) LPFRS-01/5M rubidium frequency standard supplied a common oscillator frequency to both receivers. The reference receiver was a Trimble 5700, fed by a Trimble Zephyr geodetic antenna.

    Luis Serrano is a senior navigation engineer at EADS Astrium U.K., in the Ground Segment Group, based in Portsmouth, where he leads studies and research in GNSS high precision applications and GNSS anti-jamming/spoofing software and patents. He is also a completing his Ph.D. degree at the University of New Brunwick (UNB), Fredericton, Canada.

    Don Kim is an adjunct professor and a senior research associate in the Department of Geodesy and Geomatics Engineering at UNB where he has been doing research and teaching since 1998. He has a bachelor’s degree in urban engineering and an M.Sc.E. and Ph.D. in geomatics from Seoul National University. Dr. Kim has been involved in GNSS research since 1991 and his research centers on high-precision positioning and navigation sensor technologies for practical solutions in scientific and industrial applications that require real-time processing, high data rates, and high accuracy over long ranges with possible high platform dynamics.

    FURTHER READING

    • Authors’ Proceedings Paper
    “Multipath Adaptive Filtering in GNSS/RTK-Based Machine Automation Applications” by L. Serrano, D. Kim, and R.B. Langley in Proceedings of PLANS 2010, IEEE/ION Position Location and Navigation Symposium, Indian Wells, California, May 4–6, 2010, pp. 60–69, doi: 10.1109/PLANS.2010.5507201.

    • Pseudorange and Carrier-Phase Multipath Theory and Amelioration Articles from GPS World
    “It’s Not All Bad: Understanding and Using GNSS Multipath” by A. Bilich and K.M. Larson in GPS World, Vol. 20, No. 10, October 2009, pp. 31–39.

    “Multipath Mitigation: How Good Can It Get with the New Signals?” by L.R. Weill, in GPS World, Vol. 14, No. 6, June 2003, pp. 106–113.

    “GPS Signal Multipath: A Software Simulator” by S.H. Byun, G.A. Hajj, and L.W. Young in GPS World, Vol. 13, No. 7, July 2002, pp. 40–49.

    “Conquering Multipath: The GPS Accuracy Battle” by L.R. Weill, in GPS World, Vol. 8, No. 4, April 1997, pp. 59–66.

    • Dual Antenna Carrier-phase Multipath Observable
    “A New Carrier-Phase Multipath Observable for GPS Real-Time Kinematics Based on Between Receiver Dynamics” by L. Serrano, D. Kim, and R.B. Langley in Proceedings of the 61st Annual Meeting of The Institute of Navigation, Cambridge, Massachusetts, June 27–29, 2005, pp. 1105–1115.

    “Mitigation of Static Carrier Phase Multipath Effects Using Multiple Closely-Spaced Antennas” by J.K. Ray, M.E. Cannon, and P. Fenton in Proceedings of ION GPS-98, the 11th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, September 15–18, 1998, pp. 1025–1034.

    • Digital Differentiation
    “Digital Differentiators Based on Taylor Series” by I.R. Khan and R. Ohba in the Institute of Electronics, Information and Communication Engineers (Japan) Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E82-A, No. 12, December 1999, pp. 2822–2824.

    • Autoregressive Models and the Yule-Walker Equations
    Random Signals: Detection, Estimation and Data Analysis by K.S. Shanmugan and A.M. Breipohl, published by Wiley, New York, 1988.

    • Kalman Filtering and Dynamic Models
    Introduction to Random Signals and Applied Kalman Filtering: with MATLAB Exercises and Solutions, 3rd edition, by R.G. Brown and P.Y.C. Hwang, published by Wiley, New York, 1997.

    “The Kalman Filter: Navigation’s Integration Workhorse” by L.J. Levy in GPS World, Vol. 8, No. 9, September 1997, pp. 65–71.

  • Innovation: MBOC Signal Options

    Innovation: MBOC Signal Options

    Performance of Multiplexed Binary Offset Carrier Modulations for Modernized GNSS Systems

    By E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen

    A candidate for modernized GNSS civil signals in the L1/E1 band was BOC(1,1), a binary-offset-carrier signal with a “split spectrum” that has negligible impact on the existing GPS signals. However, a signal with better acquisition capabilities and improved multipath performance (while still compatible with the existing GPS signals) is a multiplexed BOC modulation, MBOC(6,1,1/11). The MBOC spectrum can be achieved by following one of several different signal-construction paths with some resulting differences in how a receiver tracks the signal and its associated performance.

    GPS World photo
    INNOVATION INSIGHTS by Richard Langley

    IN GEOFFREY CHAUCER’S 1391 ESSAY, A Treatise on the Astrolabe (one of the earliest known instruction manuals in English), he says (with modern spelling) “Right as diverse paths lead the folk the right way to Rome.” He was talking about the use of English rather than Latin or another language to convey the same information. And we now commonly use the shortened version of this expression — all roads lead to Rome — to express the sentiment that a particular problem can be solved in different ways.

    So it was with the decision by the United States and Europe to use a common, interoperable signal for the new GPS III civil service and the Galileo Open Service on the L1/E1 frequency of 1575.42 MHz. The road to “Rome” was tedious, long, and a little bumpy at times. A number of studies and a lot of rhetoric centered on how to make the new signal compatible with the legacy GPS L1 signals, the C/A-code and the P(Y)-code, as well as the modernized GPS military signal on L1, the M-code.

    A similar compatibility issue had been solved when the M-code was added to the legacy GPS signals, starting with the Block IIR-M satellites. The M-code is a binary-offset-carrier (BOC) signal — a split spectrum signal — that places most of its power near the edges of the allocated GPS frequency bands, thereby having negligible impact on the legacy signals. The M-code modulation, designated BOC(10.23,5.115) and commonly abbreviated BOC(10,5), uses a subcarrier frequency of 10.23 MHz and a spreading code rate of 5.115 megachips per second to achieve the desired spectral separation. This design provides military users with an improved signal with little impact on civil users.

    Similar approaches were initially proposed for the new GPS L1C and Galileo E1/L1 OS signals with a BOC(1,1) modulation initially agreed on. However, further studies showed that a signal with better acquisition capabilities and improved multipath performance (while still compatible with the existing GPS signals) was a multiplexed BOC modulation, MBOC(6,1,1/11), formed by multiplexing a wideband signal, BOC(6,1), with a narrow-band signal, BOC(1,1), in such a way that 1/11th of the power is allocated, on average, to the high frequency component. Such a signal has the added benefit that one can choose whether to make use of just the low-frequency component in, say, a simple “mass market” receiver or also use the high-frequency component for more demanding applications.

    It turns out that the agreed-upon MBOC spectrum can be achieved by following one of several different signal-construction paths with some resulting differences in how a receiver tracks the signal and its associated performance. In this month’s column, we take a look at some of the options.

    In July 2007, the United States and Europe announced agreement on the use of the multiplexed binary offset carrier (MBOC) modulation as a common baseline for Galileo Open Service signals in the E1 band and GPS L1C signals in the L1 band. According to the most recent Galileo Signal-In-Space Interface Control Document (SIS-ICD; see Further Reading), the MBOC power spectral density (PSD) has been fixed to

    Innovation-Eq-1 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen(1)

    where GBOC(m,n)(f) is the normalized PSD of a BOC(m,n)-modulated pseudorandom noise (PRN) code with sine phasing. The indices m and n are related to the sub-carrier frequency, fsc, and the chip frequency, fc, via m = fsc/fref and n = fc/fref, respectively; fref = 1.023 MHz is the reference C/A-code frequency, and NB = 2fsc/fc = 2m/n is the BOC modulation index.

    The MBOC PSD is obtained by taking the data and pilot channels together. The data and pilot channels can use, independently, one of the following modulations: composite binary offset carrier (CBOC) or time-multiplexed binary offset carrier (TMBOC) modulations. CBOC and TMBOC, in turn, have several variants. Since the data and pilot channels are typically processed independently, it is important to understand the differences between various CBOC and TMBOC modulations and this is the primary goal of this article. There are several possible ways to achieve a PSD as given in Equation (1) and they are based on combining the data and pilot channels in the Galileo and modernized GPS systems. The main modulation types for pilot or data channels that can be used in order to achieve (when combined) the MBOC PSD can be summarized as follows:

    1. The CBOC method: CBOC is formed via a weighted sum or difference of BOC(1,1)- and BOC(6,1)-modulated code symbols (where the BOC(1,1) part is passed through a delay block in order to match the rate of the BOC(6,1) part) as defined in Equation (2):

    Innovation-Eq-2 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen(2)

    where sBOC(1,1),h is the up-sampled BOC(1,1)-modulated code (that is, the code provided at the same rate as the sBOC(6,1) signal), sBOC(6,1) is the BOC(6,1)-modulated code, and w1 and w2 are amplitude weighting factors, chosen in such a way to match (as closely as possible, when both data and pilot channels are considered) the PSD of Equation (1), with w12 + w22 = 1. When the two right-hand terms are added in Equation (2), CBOC(+) is formed; when subtracted, CBOC(–) is formed. A third alternative for CBOC implementation is to use the CBOC(+/–) approach, where the odd-numbered chips are CBOC(+)-modulated and the even chips are CBOC(–)-modulated. The current Galileo SIS-ICD uses a CBOC(+) variant (also called CBOC in-phase) for the E1-B data channel and a CBOC(–) variant (also called CBOC anti-phase) for the E1-C data-less (or pilot) channel.

    2. The time-multiplexed BOC (TMBOC) method: the whole signal is divided into blocks of N code symbols with M (<N) code symbols sine-BOC(1,1)-modulated, while N-M code symbols are sine-BOC(6,1)-modulated. The typical shorthand notation for this variety of TMBOC would be TMBOC(6,1,(N-M)/N), referring to the sine-BOC(6,1) component of the signal. This time-domain division may be applied for both pilot and data channels, individually. The choice of the N and M parameter values depends on the desired power percentage of the pilot channel with respect to the data channel. We have shown in earlier work (see Further Reading) that, from the point of view of the MBOC autocorrelation function, TMBOC and CBOC(+) implementations are equivalent, as long as the weights are related to the N and M values using w1 = √(M/N) and w2 = √((N-M)/N). Various TMBOC implementations exist according to the values chosen for N and M and according to whether the BOC(1,1) code symbols are in phase or out of phase with the BOC(6,1) code symbols. For example, for a 50-percent/50-percent power split between the pilot and data channels using in-phase code symbols, M = 9 and N = 11 (that is, TMBOC(6,1,2/11) is used), while for a 75-percent/25-percent power split between the pilot and data channels (again, using in-phase code symbols), M = 29 and N = 33 (that is, TMBOC(6,1,4/33) is used).

    A major difference between CBOC and TMBOC signals is that CBOC signals have four different levels (as a weighted sum or difference of two sub-carriers), while TMBOC signals have only two levels. The impact of these differences in the tracking stage of a receiver has been analyzed, for example, by a team of researchers led by Olivier Julien (see Further Reading). They showed that an optimal CBOC receiver should generate a local replica that also has four levels, resulting in a replica encoded on more than just one bit. This complicates the CBOC receiver architecture, compared to TMBOC 1-bit receiver architectures. In terms of performance, a CBOC(–) receiver proved to have the same delay-tracking variance performance as a TMBOC(6,1,4/33) receiver and both slightly outperform a TMBOC(6,1,1/11) receiver. And considering multipath error performance, a TMBOC(6,1,4/33) receiver was shown to give the best performance, followed very closely by a CBOC(–) receiver. Our research extends this earlier study.

    Examples of CBOC and TMBOC waveforms are shown in Figure 1. Here, w1 = (10/11) and the TMBOC waveform has every first chip BOC(6,1)-modulated (inside blocks of 11 chips). In the figure, only the first five modulated chips are shown for clarity.

    Fig1 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen
    Figure 1. Example of MBOC waveforms for a PRN sequence [1, -1, 1, -1, -1].
    Our article addresses the following issues: First, we analyze the spectral differences between various CBOC and TMBOC modulations in terms of their effect on receiver performance. Secondly, we look at the navigation data error probability, the tracking error variance in the presence of noise, and the robustness of the signal in the presence of multipath and bandwidth limitations of MBOC variants, by taking into account the spectral differences between the different variants. Thirdly, we justify the choice of CBOC(+) for data channels and CBOC(–) for pilot channels in the Galileo SIS-ICD in terms of these receiver performance criteria.

    Spectral Differences of CBOC/TMBOC Modulations

    The spectral differences refer to the differences in the PSD of various waveforms. We recall that the PSD is the Fourier transform of the CBOC/TMBOC autocorrelation function. CBOC/TMBOC signals are formed from the convolution of PRN code waveforms, CBOC/TMBOC modulation waveforms, and navigation data (when present). If the same PRN code is used for the BOC(1,1) and BOC(6,1) modulations, some cross-correlation terms appear in the autocorrelation function, which will also appear in the frequency spectrum. Indeed, following the model, after straightforward derivations, we obtain the generic CBOC/TMBOC PSD as:

    Innovation-Eq-3 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen(3)

    where HBOC(1,1),h(f) and HBOC(6,1)(f) are the following Fourier transforms of the modulation waveforms:

    Innovation-Eq-4 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen(4)

    Innovation-Eq-5 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen(5)

    Above, TB = TC/12 is the BOC(6,1) sub-interval and sinc(x) = sin(x)/x. The formula given in Equation (3) covers all CBOC/TMBOC cases: k = +1 for CBOC(+) and TMBOC, k = –1 for CBOC(–), and k = 0 for CBOC(+/–), respectively. Equation (3) characterizes either the pilot channel’s PSD or the data channel’s PSD. In order to achieve the PSD of Equation (1), data and pilot channels should be combined. For example, if k = 0, any combination of data and pilot channels is possible in order to attain the PSD. If k ≠ 0, then the data channel should use in-phase combining (k = +1) and the pilot channel should use anti-phase combining (k = –1) or vice versa.

    Now, if we take as a reference the PSD of CBOC(+/–) (which, incidentally, is also the PSD of Equation (1)), the spectral differences between the other CBOC/TMBOC modulations and CBOC(+/–) are quantized by the following equation:

    Innovation-Eq-6 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen(6)

    Examples of spectral difference between CBOC(+/–) and each of the following modulations: CBOC(–), CBOC(+), and TMBOC(6,1,(N-M)/N) and each of the following modulations: CBOC(–), CBOC(+), and TMBOC(6,1,(N-M)/N), respectively, are shown in Figure 2. Clearly, these differences are very small.

    Fig2 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen
    Figure 2. Examples of PSD spectral differences (linear scale) between various CBOC/TMBOC implementations and CBOC(+/-) assuming an MBOC receiver.

    Impact on System Performance

    As mentioned before, pilot and data channels typically use different CBOC/TMBOC modulations, in order to achieve an overall PSD as described by Equation (1). Now, based on the derivations we have presented so far, the following questions can be addressed: Which are the most suitable modulations (among the four discussed here; namely, CBOC(+), CBOC(–), CBOC(+/–), and TMBOC) to be used for a pilot channel and for a data channel, respectively; and how will the differences in the PSDs affect the error probability of the decoded signal and the tracking performance of each channel?

    Uncoded Error Probability and Fractional Out-of-Band Energy. Data and pilot channels are usually processed independently and then combined (for example, non-coherently) in order to perform the line-of-sight (LOS) signal delay estimation and the navigation data detection. Since different CBOC or TMBOC modulations can be used for the data and pilot channels, one question to be addressed here is what is the most suitable modulation type. Additionally, the carrier-to-noise-density ratio (C/N0) deterioration when another modulation type is employed is also important. These two issues are addressed in this section.

    One important spectral parameter that allows us to answer the question about error probability in the decoded data is the so-called fractional out-of-band energy (FOBE), which tells us about the fraction of the signal power remaining outside a certain double-sided bandwidth, Bw. FOBE is related to the power containment factor, used by some authors, via (1 – FOBE(Bw)). Clearly, FOBE depends on the signal modulation type. The higher FOBE is, the greater the deterioration of the signal energy we have after the receiver bandwidth limiting filters, and thus the higher error probability of the decoded signal we have. From the data-channel point of view, correctly decoding the navigation data is very important and therefore, low FOBE is the most important characteris
    tic when choosing the modulation type. The bit error probability in decoding a binary signal, such as a BOC or MBOC signal, can be computed by taking into account the signal energy deterioration due to filtering. Using the basic formula for computing the bit error probability in decoding a 2-level signal (in the cases of BOC or TMBOC modulation) or a 4-level signal (in the case of CBOC modulation), we can compare the performance of various TMBOC and CBOC modulations in terms of error probability of the decoded data bits, as shown in Figure 3. Clearly, the error probability criterion is more important for a data channel than for a pilot channel. Sine-BOC(1,1) and BOC(6,1) modulations are included in the comparison of Figure 3 as benchmarks. A double-sided bandwidth of 24.552 MHz was considered here, following the choice in the Galileo SIS-ICD.

    Figure 3. Detection error probability for CBOC/TMBOC-modulated signals with a 24.552 MHz double-sided bandwidth. Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen
    Figure 3. Detection error probability for CBOC/TMBOC-modulated signals with a 24.552 MHz double-sided bandwidth.

    As seen in Figure 3, in terms of the error probability of the decoded signal, BOC(1,1) modulation gives the best results, followed closely by TMBOC(6,1,4/33). In order to achieve an error probability of 10-2, the CNR differences shown in Table 1 are needed for the different modulation types. From Table 1, it can be seen that, among CBOC modulations, the CBOC(+) modulation is the best option from the point of view of decoding the data, and, therefore, it makes it a suitable option for data channels, as chosen in the Galileo SIS-ICD. We remark that the huge CNR gap for BOC(6,1) at Bw = 8 MHz is due to the fact that the power containment of a BOC(6,1) signal is very poor at such a low bandwidth.

    Inn-Table-1 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen

    Gabor Bandwidth and Tracking Error Variance. Another important spectral parameter of interest in this analysis is the root-mean-square (RMS) or Gabor bandwidth. A larger RMS or Gabor bandwidth permits a higher accuracy against thermal noise and the tracking accuracy is approximately inversely proportional to the RMS bandwidth. The code-tracking error variance is an important parameter when trying to achieve accurate location estimates. Indeed, a Cramér-Rao lower bound (CRLB) on the tracking error variance has been derived by other researchers. Following the derivation for CRLB on the tracking error variance, we can also compare the performance of various CBOC and TMBOC modulations, as presented in Figure 4. Clearly, this criterion is more important for a pilot channel than for a data channel. A double-sided receiver bandwidth of 24.552 MHz was considered here.

    Fig4 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen
    Figure 4. Cramér-Rao lower bound on tracking error variance (in seconds2) for CBOC/TMBOC-modulated signals with a 24.552 MHz double-sided bandwidth.

    In terms of the tracking error variance bound, which linearly decreases with the CNR (on a dB scale), the CNR differences between various modulations are shown in TablE 2 for a 4-Hz tracking-loop bandwidth. Clearly, from Table 2, CBOC modulations are better in terms of tracking error variance than TMBOC modulation, and, among the CBOC variants, CBOC(–) has the best performance. This justifies the fact that the Galileo SIS-ICD has chosen the CBOC(–) as the best option for pilot channels. We can also see in Table 2 that the bandwidth limitation has an important effect on the tracking error bounds, as expected. At low receiver bandwidth (such as 8 MHz), the differences between various modulations are rather small, while at high or infinite bandwidths, BOC(6,1) modulation is by far the best option, followed by CBOC(–) with a 1.69 dB gap in CNR (that is, CBOC(–) requires an additional 1.69 dB in order to achieve the same tracking error performance as BOC(6,1)).

    Inn-Table-2 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen

    Multipath Error Envelope. The typical procedure for evaluating the performance of a multipath-mitigation technique is via the multipath error envelope (MEE). The MEE curves are obtained for two extreme phase variations of a multipath signal with respect to the LOS component while varying the multipath (that is, second path) delays from 0 to 1.2 chips at maximum, since the multipath errors become less significant after that. The upper multipath error envelope can be obtained when the paths are in-phase (that is, 0° phase difference) and the lower multipath error envelope when the paths are out-of-phase (that is, 180° phase difference). In MEE analysis, several simplifying assumptions are usually made in order to distinguish the performance degradation caused by the multipath only. Such assumptions include zero additive white Gaussian noise, ideal infinite-length PRN codes, zero residual Doppler shift, and zero initial code-delay error.

    The MEE curves are generated here for different variants of MBOC implementation. The multipath performance of these MBOC variants with a BOC(1,1)-modulated reference receiver is also presented. In the MEE generation, the second path amplitude was fixed at 3 dB lower than the LOS component. The MEE curves were generated for a 24.552 MHz double-sided bandwidth. The narrow early-minus-late (nEML) correlator with an early-late correlator spacing of 0.0833 chips was used here as a tool for evaluating the performance of the different MBOC variants in the presence of multipath. The nEML is based on the idea of narrowing the spacing between the early and late correlator pair, where the choice of correlator spacing depends on the receiver’s available front-end bandwidth along with the associated sampling frequency.

    MEE curves are shown for all of the examined MBOC variants in Figure 5. It can be observed from the figure that CBOC(–) has the best multipath mitigation performance followed by the TMBOC(6,1,4/33) and CBOC(+) variants. A similar conclusion can be drawn when a BOC(1,1) reference receiver is used instead of the respective MBOC reference receiver. However, from Figure 5, it is obvious that there is a moderate performance degradation when a BOC(1,1) reference receiver is used instead of the respective MBOC version, as expected intuitively.

    Figure 5. Multipath error envelope curves for a narrow early-minus-late correlator with a 24.552 MHz double-sided bandwidth. Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen
    Figure 5. Multipath error envelope curves for a narrow early-minus-late correlator with a 24.552 MHz double-sided bandwidth.

    Simulation Results in Multipath Fading Channel

    Simulations have been carried out in closely spaced multipath scenarios for different MBOC variants with a finite front-end bandwidth. The simulation profile is summarized in Table 3. A Rayleigh fading channel model is used in the simulation, where the number of channel paths is fixed to two. The successive path separation is random between 0.02 and 0.35 chips. The channel paths are assumed to obey a decaying power delay profile (PDP).

    Inn-Table-3 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen

    The received signal duration is 0.8 seconds for each particular C/N0 level. The tracking errors are computed after each NcNnc-milliseconds interval (in this case, NcNnc = 20 milliseconds). In the final statistics, the first 600 milliseconds are ignored in order to remove the initial error bias that may come from the delay difference between the received signal and the locally generated reference code. Therefore, for the above configuration, the left-over tracking errors after 600 milliseconds are mostly due to the effect of multipath only. We ran the simulations for 1,000 statistical points, for each C/N0 b> level. The RMS error (RMSE) of the delay estimates can be plotted in meters, by using the relationship RMSEm = RMSEchips•c•Tc, where c is the speed of light, Tc is the chip duration, and RMSEchips is the RMSE in chips. An RMSE versus C/N0 plot for the given multipath channel profile is shown in Figure 6.

    As seen in the figure, the CBOC(–) reference receiver has the best multipath mitigation performance under a good

    C/N0 (that is, 40 dB-Hz and higher) followed by the other two MBOC variants (CBOC(+) and TMBOC(6,1,4/33)), which exhibit almost similar performance. A similar conclusion can be drawn for the BOC(1,1) reference receiver, where the CBOC(–)-modulated transmitted signal with BOC(1,1) reference receiver showed the best multipath mitigation performance among all three of the studied MBOC variants. In Figure 6, we observe that the small performance deterioration caused by use of a BOC(1,1) reference receiver is visible only under good C/N0 conditions (that is, 40 dB-Hz and higher).

    Fig6 Source: E. Simona Lohan, Mohammad Z. H. Bhuiyan, and Heikki Hurskainen
    Figure 6. Root-mean-square error versus carrier-to-noise-density ratio for different MBOC variants in a two-path fading channel with 24.552 MHz double-sided bandwidth.

    Conclusions

    This article discusses the spectral differences between CBOC and TMBOC modulations and their impact on system performance. The exact frequency-domain form of the PSD for CBOC and TMBOC waveforms has been shown and the impact on tracking error variance bounds and on the error probability of the demodulated signal has been discussed. In addition, the multipath mitigation performances of different MBOC variants were presented in terms of RMSE and multipath error envelopes. It was shown that the CBOC(–) variant is the best variant in terms of multipath mitigation and tracking error variance, while TMBOC behaves better than CBOC in terms of error probability of the demodulated data. We also showed that the spectral differences and the differences between CBOC and TMBOC variants in terms of the two considered performance criteria are rather small, especially when the receiver bandwidth is not very high, and, therefore, several variants of MBOC can indeed be used for design and research purposes.

    Acknowledgments

    The research leading to the results presented in this article received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement number 227890 (the Galileo-Ready Advanced Mass Market Receiver–GRAMMAR–project). This research work has also been supported by the Academy of Finland and by the Tampere Doctoral Programme in Information Science and Engineering. Particular thanks are also addressed to Stephan Sand from the German Aerospace Center (DLR), Institute of Communications and Navigation, for his useful comments.

    Elena Simona Lohan has been an adjunct professor in the Department of Communications Engineering at Tampere University of Technology (TUT) in Hervanta, Finland, since 2007. She obtained her Ph.D. degree in wireless communications from TUT. She also graduated with an M.Sc. in electrical engineering from “Politehnica” University of Bucharest, and with a diplôme d’études approfondies in econometrics from Ecole Polytechnique, Paris. Lohan is currently leading the research activities in signal processing for wireless communications in the Department of Communications Engineering at TUT.

    Mohammad Zahidul H. Bhuiyan is a researcher in the Department of Communications Engineering at TUT. His main research areas are multipath mitigation and software receiver design for satellite-based positioning applications.

    Heikki Hurskainen received an M.Sc. degree in electrical engineering and a doctoral degree in computing and electrical engineering from TUT in 2005 and 2009, respectively. Currently, Hurskainen is a senior research scientist in TUT’s Department of Computer Systems where he works on satellite navigation research projects.

     

    FURTHER READING

    • Galileo and Modernized GPS Signal Definitions and Policies
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    Navstar GPS Space Segment/User Segment L1C Interfaces, Rev. A, Interface Specification, IS-GPS-800A, prepared by Science Applications International Corporation, El Segundo, California for the Global Positioning System Wing, Systems Engineering and Integration, Los Angeles Air Force Base, California, June 2010.

    • Binary Offset Carrier Modulation
    “Low Complexity Unambiguous Acquisition Methods for BOC-modulated CDMA Signals” by E.S. Lohan, A. Burian, and M. Renfors in International Journal of Satellite Communications and Networking, Vol. 26, No. 6, 2008, pp. 503–522, doi: 10.1002/sat.922.

    “Binary-Offset-Carrier Modulation Techniques with Applications in Satellite Navigation Systems” by E.S. Lohan, A. Lakhzouri, and M. Renfors in Wireless Communications and Mobile Computing, Vol. 7, No. 6, 2007, pp. 767–779, doi: 10.1002/wcm.407.

    “Overview of the GPS M Code Signal” by B.C. Barker, J.W. Betz, J.E. Clark, J.T. Correia, J.T. Gillis, S. Lazar, K.A. Rehborn, and J.R. Straton, III, in Proceedings of 2000: Navigating into the New Millennium, the 2000 National Technical Meeting of The Institute of Navigation, Anaheim, California, January 26–28, 2000, pp. 542–549.

    “The Offset Carrier Modulation for GPS Modernization” by J.W. Betz, in Proceedings of Vision 2010: Present and Future, the 1999 National Technical Meeting of The Institute of Navigation and 19th Biennial Guidance Test Symposium, San Diego, California, January 25–27, 1999, pp. 639-648.

    • Multiplexed Binary Offset Carrier Modulation Implementations and Comparisons
    “Future Wave: L1C Signal Performance and Receiver Design” by T.A. Stansell, K.W. Hudnut, and R.G. Keegan in GPS World, Vol. 22, No. 4, April 2011, pp. 30–36,41.

    “Analytical Performance of CBOC-modulated Galileo E1 Signal Using Sine BOC(1,1) Receiver for Mass-market Applications” by E.S. Lohan, in Proceedings of PLANS 2010, IEEE/ION Position Location and Navigation Symposium, Indian Wells, California, May 4–6, 2010, pp. 245–253, doi: 10.1109/PLANS.2010.5507207.

    “MBOC and BOC(1,1) Performance Comparison” by N. Hoult, L.E. Aguado, and P. Xia in The Journal of Navigation, Vol. 61, No. 4, October 2008, pp. 613–627, doi: 10.1017/S0373463308004918.

    “The MBOC Modulation: A Final Touch for the Galileo Frequency and Signal Plan” by J.A. Avila-Rodriguez, G.W. Hein, S. Wallner, J.L. Issler, L. Ries, L. Lestarquit, A. De Latour, J. Godet, F. Bastide, T. Pratt, and J. Owen in Inside GNSS, Vol. 2, No. 6, Se
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    “Two for One: Tracking Galileo CBOC Signal with TMBOC” by O. Julien, C. Macabiau, J.L. Issler, and L. Ries in Inside GNSS, Vol. 2, No. 3, Spring 2007, pp. 50–57.

    “MBOC: The New Optimized Spreading Modulation Recommended for Galileo L1 OS and GPS L1C” by G.W. Hein, J.A. Avila-Rodriguez, S. Wallner, J.W. Betz, C.J. Hegarty, J.J. Rushanan, A.L. Kraay, A.R. Pratt, S. Lenahan, J. Owen, J.L. Issler, and T.A. Stansell in Inside GNSS, Vol. 1, No. 4, May-June 2006, pp. 57–65.

    • Gabor Bandwidth and Cramér-Rao Bound
    Spread Spectrum Systems for GNSS and Wireless Communications by J.K. Holmes, published by Artech House, Inc., Norwood, Massachusetts, 2007.

    “Multipath Mitigation: How Good Can It Get with the New Signals?” by L.R. Weill in GPS World, Vol. 14, No. 6, June 2003, pp. 106–113.

    “A Family of Split Spectrum GPS Civil Signals” by J.J. Spilker, Jr., E.H. Martin, and B.W. Parkinson, in Proceedings of ION GPS-98, the 11th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, September 15–18, 1998, pp. 1905–1914.

    • Narrow Early-Minus-Late Correlation
    “Extended Theory of Early-Late Code Tracking for a Bandlimited GPS Receiver” by J.W. Betz and K.R. Kolodziejski in Navigation: Journal of the Institute of Navigation, Vol. 47, No. 3, 2000, pp. 211–226.