Loctronix Corporation, a provider of unified positioning solutions for GNSS-challenged environments, has announced the availability of the ASR Workbench, a development toolset for the company’s recently released ASR-2300 software-defined radio (SDR) module. The ASR-2300 is a function-rich SDR for high-performance positioning, navigation and timing (PNT), and communication applications, the company said.
“The ASR-2300 delivers advanced SDR capabilities in a small, mobile form-factor enabling developers to readily create and field complex SDR-based solutions. The new ASR Workbench tool makes it easy for developers to take full advantage of the ASR-2300’s capabilities,” said Michael Mathews, Loctronix’ CEO and founder.
The new ASR Workbench is a Windows-based Integrated Development Environment (IDE) for SDR applications development and testing. It comes with a drag-and-drop, real-time DSP modeling tool with integrated support for the ASR-2300. With ASR Workbench developers will be able to:
Process multiple ASR-2300 baseband I/Q sample streams.
Access a variety of DSP processing and visualization blocks for use in custom models.
Record/playback signals, analyze received signals using a variety of demonstration models.
Optimize the performance and configuration of the ASR-2300 module with a suite of diagnostic tools.
Export data into formats supporting additional analysis using a variety of standard tools including Matlab/Simulink, Excel, etc.
Loctronix’ ASR-2300 SDR module provides multiple, fully-integrated RF paths supporting reception of GNSS, cellular, ISM band, and UHF signals of opportunity, making it suitable for demanding scientific, military, aerospace and commercial/industrial applications, such as UAV/UAS navigation, GPS-challenged or -denied tracking and navigation, combined communications and navigation radios, and GPS integrity monitoring and validation, according to Mathews.
“Using an SDR effectively is challenging due to the steep learning curve required to take advantage of its many programmability benefits. At Loctronix, one of our highest priorities is to provide tools that simplify complex application development. It is not enough to provide just an API and hardware for the user community and hope that they will learn how to use the platform effectively,” Mathews said.
“Developers looking to create solutions for these demanding applications will realize greater functionality with the ASR-2300, thanks to its multiple sensor and multiple frequency capabilities,” he added. “The new ASR Workbench will result in shorter development times and lower development costs for such high-performance PNT applications.”
The ASR Workbench will be freely downloadable for customers purchasing the ASR-2300. The ASR-2300 SDR is available directly from Loctronix.
CHC announced today the availability of CHC Geomatics Office (CGO), a software solution dedicated to post processing static and kinematic GNSS raw data. CGO supports GPS+GLONASS+BeiDou data in various raw data formats and is compatible with major brands, allowing a seamless integration with an existing pool of equipment, the company said.
“CGO is undoubtedly the most affordable yet powerful GNSS post processing software available in the market.” says George Zhao, CEO of CHC. “In addition, this new product launch reinforces our commitment to provide full GNSS solutions to our customers including post-processing applications.”
A 90-day fully functional demonstration license is available to enable users to evaluate the CGO’s features before purchasing.
CHC designs, manufactures and markets a wide range of professional GPS/GNSS solutions in more than 50 countries. Headquartered in Shanghai (China), CHC is a GPS/GNSS manufacturer with a strong international presence and employs more than 500 professionals worldwide.
The implementation changes and first live tests of BeiDou and Galileo on Teseo-3 GNSS chips developed in 2013 are covered, bringing it to a four-constellation machine. By 2020, we expect to have four global constellations all on the same band, giving us more than 100 satellites — under clear sky, as many as 30 or 40 simultaneously.
By Philip G. Mattos and Fabio Pisoni
Multi-constellation GNSS first became widely available in 2010/2011, but only as two constellations, GPS+GLONASS. Although receivers at that time may have supported Galileo, there were no usable satellites. BeiDou was a name only, as without a spec (an interface control document, or ICD), no receivers could be built. However, the hardware development time of receivers had been effectively shortened: the Galileo ICD had been available for years, BeiDou codes had been reverse-engineered by Grace Gao and colleagues at Stanford, and at the end of 2011 they were confirmed by the so-called test ICD, which allowed signal testing without yet releasing message characteristics or content.
The last weeks of 2012 saw two great leaps forward for GNSS. Galileo IOV3 and 4 started transmitting at the beginning of December, bringing the constellation to four and making positioning possible for about two hours a day. At the end of December, the Chinese issued the BeiDou ICD, allowing the final steps of message decode and ephemeris calculation to be added to systems that had been tracking BeiDou for many months, and thus supporting positioning. The Teseo-2 receiver from STMicroelectronics has been available for some years, so apart from software development, it was just waiting for Galileo satellites; however, for BeiDou it needed hardware support in the form of an additional RF front end. Additionally, while it could support all four constellations, it could not support BeiDou and GPS/Galileo at the same time, as without the BeiDou ICD the spreading codes had to be software-generated and used from a memory-based code generator, thus blocking the GPS/Galileo part of the machine.
The Teseo-3 receiver appeared late in 2013, returning to the optimum single-chip form factor: RF integrated with digital silicon and flash memory in the same package, enabling simultaneous use of BeiDou and GPS/Galileo signals. Multi-constellation in 2012 was GPS+GLONASS, which brought huge benefits in urban canyons with up to 20 visible satellites in an open sky. Now, for two hours a day in Europe while the Galileo IOVs are visible, we can run three constellations, and in the China region, GPS/BeiDou/Galileo is the preferred choice.
This article covers the first tracking of four Galileo satellites on December 4, 2012, first positioning with Galileo, and first positioning with BeiDou in January 2013. It will cover static and road tests of each constellation individually and together as a single positioning solution. Road tests in the United States/Europe will combine GPS/GLONASS/Galileo, while tests in the China region will combine GPS/Galileo/BeiDou. Results will be discussed from a technical point of view, while the market future of multi-constellation hardware will also be considered.
In the 2010–2020 timeframe, GLONASS and BeiDou (1602 MHz FDMA and 1561 MHz respectively) cost extra silicon in both RF and digital hardware, and cause marginal extra jamming vulnerability due to the 50 MHz bandwidth of the front end. The extra silicon also causes extra power consumption.
After 2020, GLONASS is expected to have the L1OC signal operational, CDMA on the GPS/Galileo frequency, and BeiDou is expected both to have expanded worldwide, and also to have the B3 signal fully operational, again on 1575 MHz. At that point we will have four global constellations all on the same band, giving us more than 100 satellites. With a clear sky, the user might expect to see more than 30, sometimes 40, satellites simultaneously.
Besides the performance benefits in terms of urban canyon availability and accuracy, this allows the receiver to be greatly simplified. While code generators will require great flexibility to generate any of the code families at will, the actual signal path will be greatly simplified: just one path in both RF (analog) and baseband (digital) processing, including all the notch filters, derotation, and so on. And this will greatly reduce the power consumption.
Will the market want to take the benefit in power consumption and silicon area, or will it prefer to reuse those resources by becoming dual-frequency, adding also the lower-L-band signals, initially L5/E5, but possibly also L2/L3/L6 ? The current view is that the consumer receiver will go no further than L5/E5, but that the hooks will be built-in to allow the same silicon to be used in professional receivers also, or in L2C implementations to take advantage of the earlier availability of a full constellation of GPS-L2C rather than GPS-L5.
This article presents both technical results of field trials of the quad-constellation receiver, and also the forward looking view of how receivers will grow through multi-frequency and shrink through the growing signal commonalities over this decade.
History
Galileo was put into the ST GPS/GNSS receiver hardware from 2006 to 2008, with a new RF and an FPGA-based baseband under the EU-funded GR-PosTer project. While a production baseband (Cartesio-plus) followed in high volume from 2009, in real life it was still plain GPS due to the absence of Galileo satellites.
The changed characteristics in Galileo that drove hardware upgrades are shown in Figure 1. The binary offset carrier BOC(1,1) modulation stretches the bandwidth, affecting the RF, while both the BOC and the memory codes affect the baseband silicon in the code-generator area.
Figure 1. Changes for Galileo.
Next was the return to strength of the GLONASS constellation, meaning receivers were actually needed before Galileo. However the different center frequency (1602 MHz), and the multi-channel nature of the FDMA meant more major changes to the hardware. As shown in Figure 2 in orange, a second mixer was added, with second IF path and A/D converter.
Figure 2. Teseo-2 RF hardware changes for GLONASS.Figure 3. Teseo-2 and Teseo-3 baseband changes for GLONASS.
The baseband changes added a second pre-processing chain and configured all the acquisition channels and tracking channels to flexibly select either input chain. Less visible, the code-generators were modified to support 511 chip codes and 511kchips/sec rates.
Teseo-2 appeared with GPS/GLONASS support in 2010, and demonstrated the benefit of GNSS in urban canyons, as shown by the dilution of precision (DOP) plot for central London in Figure 4. The GPS-only receiver (in red) has frequent DOP excursions beyond limits, resulting either in bad accuracy or even interrupted fix availability. In contrast, the GNSS version (in blue) has a DOP generally below 1, with a single maximum of 1.4, and thus 100 percent availability. Tracking 16 satellites, even if many are via non-line-of-sight (NLOS) reflected paths, allows sophisticated elimination of distorted measurements but still continuous, and hence accurate, positioning.
Figure 4. DOP/accuracy benefits of GNSS.
BeiDou
Like Galileo, BeiDou is a story of chapters. Chapter 1 was no ICD, and running on a demo dual-RF architecture as per the schematic shown in Figure 5. Chapter 2 was the same hardware with the test ICD, so all satellites, but still no positioning. Chapter 3 was the full ICD giving positioning in January 2013 (Figure 6), then running on the real Teseo-3 silicon in September 2013, shown in Figure 7.
Figure 5. Demo Teseo-2 dual RF implementation of BeiDou.Figure 6. Beidou positioning results.Figure 7. Teseo 3 development board.
The Teseo-3 has an on-chip RF section capable of GPS, Galileo, GLONASS and BeiDou, so no external RF is needed.
The clear green space around the Teseo-3 chip in the photo and the four mounting holes are for the bolt-down socket used to hold chips during testing, while the chip shown is soldered directly to the board. Figure 8A shows the development board tracking eight BeiDou satellites visible from Taiwan.
However, the silicon is not designed to be single-constellation; it is designed to use all the satellites in the sky. Figure 8b shows another test using GPS and BeiDou satellites simultaneously.
Figure 8A. Beidou.Figure 8b. GPS+Beidou.
A mobile demo on the Teseo-3 model is shown running GPS plus BeiDou in Figure 9, a road test in Taipei. Satellites (SV) up to 32 are GPS, those over 140 are BeiDou, in the status window shown: total 13 satellites in a high-rise city area, though many are non-LOS.
Figure 9. GPS + Beidou roadtrack in Taipei.
Extending the hardware to add BeiDou, which is on 1561 MHz and thus a third center frequency, meant adding another path through the IF stages of the on-chip radio. After the first mixer, GPS is at 4 MHz, and GLONASS at about 30 MHz, but BeiDou is at minus 10 MHz. While the IF strip in general is real, rather than complex (IQ), the output of the mixer and input to the first filter stage is complex, and thus can discriminate between positive frequencies (from the upper sideband) and negative ones (from the lower sideband), and this is normally used to give good image rejection. In the case of BeiDou, the filter input is modified to take the lower sideband, that is, negative frequencies, and a second mixer is not required; the IF filter is tuned to 10 MHz. The new blocks for BeiDou are shown in green in Figure 10. The baseband has no new blocks, but the code generator has been modified to generate the BeiDou codes (and, in fact, made flexible to generate many other code types and lengths). Two forms of Teseo-3 baseband are envisaged, the first being for low-cost, low-current continues to have two input paths, so must choose between GLONASS and BeiDou as required. A future high-end model may have an extra input processing path to allow use of BeiDou and GLONASS simultaneously.
Figure 10. Teseo-3 RF changes for Beidou shown in green.
Galileo Again
Maintaining the chronological sequence, Galileo gets a second chapter in three steps. In December 2012, it was possible for the first time to track four IOV satellites simultaneously, though not to position due to the absence of valid orbit data. In March 2012, it was possible for the first time to demonstrate live positioning, and this was done using Teseo-2 simultaneously by ESA at ESTEC and STMicro in Naples and Milan, our software development centres.
The demos were repeated in public for the press on July 24, 2013, at Fucino, Italy’s satellite earth station, with ESA/EC using the test user receiver (TUR) from Septentrio, and ST running simultaneous tests at its Italian labs. Figure 11 and Figure 12 show the position results for the data and pilot channels respectively, with independent LMS fixes. In real life, the fixes would be from a Kalman filter, and would be from a combined E1-B/E1-C channel, to take advantage of the better tracking on the pilot.
Good accuracy is not expected from Galileo at this stage. The four satellites, while orbited to give good common visibility, do not also give a good DOP; the full set of ground monitoring stations is not yet implemented and cannot be well calibrated with such a small constellation. Finally, the ionospheric correction data is not yet available. Despite these problems, the residuals on the solutions, against a known fixed position for the rooftop antenna, are very respectable, shown in Figure 13.
Figure 13. Galileo residuals, L1-B.
The common mode value is unimportant, representing only an offset in the receiver clock, and 10 meters is about 30 nanoseconds. The accuracy indicator is the spread between satellites, which is very respectable for a code-only receiver without full iono correction, especially around 640 on the TOW scale, where it is less than 2 meters. The rapid and major variation on the green data around t=400 is considered to be multipath, as the roof antenna is not ideally positioned with respect to other machinery and equipment also installed on the roof.
QZSS and GPS-III/L1C
Teseo-2 has supported the legacy (C/A code) signal on QZSS for some time, but Teseo-3 has been upgraded to handle the GPS-III/L1-C signal, waiting for modernized GPS. This signal is already available on the QZSS satellite, allowing tests with real signals. Significant changes were required in the baseband hardware, as the spreading code is a Weill code, whose generation complexity is such that it is generated once when the satellite is selected, then replayed real time from memory. Additionally it is long, in two domains. It is 10230 chips — that is, long to store but also long in time, with a 10-millisecond epoch. On Teseo-3, the legacy C/A code is used to determine code-phase and frequency before handing over to the Weill code for tracking.
Using a long-range crystal ball and looking far into the future, a model of the future Teseo-4 DSP hardware is available, with 64 correlation taps per satellite. Running this on the captured QZSS L1-C signal gives the correlation response shown in Figure 14. Having multiple taps removes all ambiguity from the BOC signal, simultaneously removing data transitions, which can alternatively be pre-stripped using the known pilot secondary code (which on GPS III is 5 dB stronger than the data signal). The resultant plot represents 2,000 epochs, each of 10 milliseconds, plotted in blue, with integrated result for the full 20 seconds shown in the black dashed line. Assuming vehicle dynamics is taken out using carrier Doppler, this allows extremely precise measurement of the code phase, or analysis of any multipath in order to remove it. This RF data was captured on a benign site with a static antenna, so it shows little distortion.
Figure 14. L1-C tracking on QZSS satellite.Figure 15. Dual RF implementation of dual-band front end.
The Future
Having already built in extreme flexibility to the code generators to support all known signals and generalized likely future ones, the main step for the future is to support multiple frequencies, starting with adding L5 and/or L2, but as before, ensuring that enough flexibility is built in to allow any rational user/customer choice. It is not viable for us to make silicon for low-volume combinations, nor to divide the overall market over different chips. Thus our mainstream chip must also support the lower volume options.
We cannot, however, impose silicon area or power consumption penalties on the high-volume customer, or he will not buy our product.
Thus, our solution to multi-frequency is to make an RF that can support either band switchably, with the high band integrated on the volume single-chip GNSS. Customers who also need the low band can then add a second RF of identical design externally, connected to the expansion port on the baseband, which has always existed for diagnostic purposes, and was how BeiDou was demonstrated on T2. By being an RF of identical design to the internal one, it incurs no extra design effort, and would probably be produced anyway as a test chip during the development of the integrated single-chip version. Without this approach, the low volume of sales of a dual-band radio, or a low-band radio, would never repay its development costs.
Conclusions
All four constellations have been demonstrated with live satellite signals on Teseo-2, a high-volume production chip for several years, and on Teseo-3 including use in combinations as a single multi-constellation positioning solution. With the advent of Teseo-3, with optimized BeiDou processing and hardware support for GPS-3/L1C, a long-term single-chip solution is offered.
For the future, dual-frequency solutions are in the pipeline, allowing full advantage of carrier phase, and research into moving precise point positioning and real-time kinematic into the automotive market for fields such as advanced driver-assistance systems.
Acknowledgments
Teseo III design and development is supported by the European Commission HIMALAYA FP-7 project.
This article is based on a technical paper first presented at ION-GNSS+ 2013 in Nashville, Tennessee.
ST GPS products, chipsets and software, baseband and RF are developed by a distributed team in: Bristol, UK (system R&D, software R&D; Milan, Italy (Silicon implementation, algorithm modelling and verification); Naples, Italy (software implementation and validation); Catania, Sicily, Italy (Galileo software, RF design and production); Noida, India (verification and FPGA). The contribution of all these teams is gratefully acknowledged.
Philip G. Mattos received an external Ph.D. on his GPS work from Bristol University. Since 1989 he has worked exclusively on GNSS implementations, RF, baseband and applications. He is consulting on the next-generation GNSS chips, including one-chip GPS (RF+digital), and high-sensitivity GPS and Galileo for indoor applications, and combined GPS/Galileo/GLONASS chipsets. In 2008-2009, he re-implemented LORAN on the GPS CPU, and in 2009-2010 led the GLONASS implementation team. He is leading the team on L1C and BeiDou implementation, and the creation of totally generic hardware that can handle even future unknown systems.
Fabio Pisoni has been with the GNSS System Team at STMicroelectronics since 2009. He received a master’s degree in electronics from Politecnico di Milano, Italy, in 1994. He was previously with the GNSS DSP and System Team in Nemerix SA and has earlier working experience in communications (multi-carrier receivers).
By Malek O. Karaim, Tashfeen B. Karamat, Aboelmagd Noureldin, Mohamed Tamazin, and Mohamed M. Atia
A team of university researchers has developed a technique combining GPS receivers with an inexpensive inertial measuring unit to detect and repair cycle slips with the potential to operate in real time.
INNOVATION INSIGHTS by Richard Langley
DRUM ROLL, PLEASE. The “Innovation” column and GPS World are celebrating a birthday. With this issue, we have started the 25th year of publication of the magazine and the column, which appeared in the very first issue and has been a regular feature ever since. Over the years, we have seen many developments in GPS positioning, navigation, and timing with a fair number documented in the pages of this column.
In January 1990, GPS and GLONASS receivers were still in their infancy. Or perhaps their toddler years. But significant advances in receiver design had already been made since the introduction around 1980 of the first commercially available GPS receiver, the STI-5010, built by Stanford Telecommunications, Inc. It was a dual-frequency, C/A- and P-code, slow-sequencing receiver. Cycling through four satellites took about five minutes, and the receiver unit alone required about 30 centimeters of rack space. By 1990, a number of manufacturers were offering single or dual frequency receivers for positioning, navigation, and timing applications. Already, the first handheld receiver was on the market, the Magellan NAV 1000. Its single sequencing channel could track four satellites. Receiver development has advanced significantly over the intervening 25 years with high-grade multiple frequency, multiple signal, multiple constellation GNSS receivers available from a number of manufacturers, which can record or stream measurements at data rates up to 100 Hz. Consumer-grade receivers have proliferated thanks, in part, to miniaturization of receiver chips and modules. With virtually every cell phone now equipped with GPS, there are over a billion GPS users worldwide. And the chips keep getting smaller. Complete receivers on a chip with an area of less than one centimeter squared are common place. Will the “GPS dot” be in our near future?
The algorithms and methods used to obtain GPS-based positions have evolved over the years, too. By 1990, we already had double-difference carrier-phase processing for precise positioning. But the technique was typically applied in post-processing of collected data. It is still often done that way today. But now, we also have the real-time kinematic (or RTK) technique to achieve similar positioning accuracies in real time and the non-differenced precise point positioning technique, which does not need base stations and which is also being developed for real-time operation. But in all this time, we have always had a “fly in the ointment” when using carrier-phase observations: cycle slips. These are discontinuities in the time series of carrier-phase measurements due to the receiver temporarily losing lock on the carrier of a GPS signal caused by signal blockage, for example. Unless cycle slips are repaired or otherwise dealt with, reduction in positioning accuracy ensues. Scientists and engineers have developed several ways of handling cycle slips not all of which are capable of working in real time. But now, a team of university researchers has developed a technique combining GPS receivers with an inexpensive inertial measuring unit to detect and repair cycle slips with the potential to operate in real time. They describe their system in this month’s column.
“Innovation” is a regular feature that discusses advances in GPS technology and its 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. He welcomes comments and topic ideas.
GPS carrier-phase measurements can be used to achieve very precise positioning solutions. Carrier-phase measurements are much more precise than pseudorange measurements, but they are ambiguous by an integer number of cycles. When these ambiguities are resolved, sub-centimeter levels of positioning can be achieved.
However, in real-time kinematic applications, GPS signals could be lost temporarily because of various disturbing factors such as blockage by trees, buildings, and bridges and by vehicle dynamics. Such signal loss causes a discontinuity of the integer number of cycles in the measured carrier phase, known as a cycle slip. Consequently, the integer counter is reinitialized, meaning that the integer ambiguities become unknown again. In this event, ambiguities need to be resolved once more to resume the precise positioning and navigation process. This is a computation-intensive and time-consuming task. Typically, it takes at least a few minutes to resolve the ambiguities.
The ambiguity resolution is even more challenging in real-time navigation due to receiver dynamics and the time-sensitive nature of the required kinematic solution. Therefore, it would save effort and time if we could detect and estimate the size of these cycle slips and correct the measurements accordingly instead of resorting to a new ambiguity resolution. In this article, we will briefly review the cause of cycle slips and present a procedure for detecting and correcting cycle slips using a tightly coupled GPS/inertial system, which could be used in real time. We will also discuss practical tests of the procedure.
Cycle Slips and Their Management
A cycle slip causes a jump in carrier-phase measurements when the receiver phase tracking loops experience a temporary loss of lock due to signal blockage or some other disturbing factor. On the other hand, pseudoranges remain unaffected. This is graphically depicted in FIGURE 1. When a cycle slip happens, the Doppler (cycle) counter in the receiver restarts, causing a jump in the instantaneous accumulated phase by an integer number of cycles. Thus, the integer counter is reinitialized, meaning that ambiguities are unknown again, producing a sudden change in the carrier-phase observations.
FIGURE 1. A cycle slip affecting phase measurements but not the pseudoranges.
Once a cycle slip is detected, it can be handled in two ways. One way is to repair the slip. The other way is to reinitialize the unknown ambiguity parameter in the phase measurements. The former technique requires an exact estimation of the size of the slip but could be done instantaneously. The latter solution is more secure, but it is time-consuming and computationally intensive. In our work, we follow the first approach, providing a real-time cycle-slip detection and correction algorithm based on a GPS/inertial integration scheme.
GPS/INS Integration
An inertial navigation system (INS) can provide a smoother and more continuous navigation solution at higher data rates than a GPS-only system, since it is autonomous and immune to the kinds of interference that can deteriorate GPS positioning quality. However, INS errors grow with time due to the inherent mathematical double integration in the mechanization process. Thus, both GPS and INS systems exhibit mutually complementary characteristics, and their integration provides a more accurate and robust navigation solution than either stand-alone system. GPS/INS integration is often implemented using a filtering technique. A Kalman filter is typically selected for its estimation optimality and time-recursion properties.
The two major approaches of GPS/INS integration are loosely coupled and tightly coupled. The former strategy is simpler and easier to implement because the inertial and GPS navigation solutions are generated independently before being weighted together by the Kalman filter. There are two main drawbacks with this approach: 1) signals from at least four satellites are needed for a navigation solution, which cannot always be guaranteed; and 2) the outputs of the GPS Kalman filter are time correlated, which has a negative impact upon the system performance. The latter strategy performs the INS/GPS integration in a single centralized Kalman filter. This architecture eliminates the problem of correlated measurements, which arises due to the cascaded Kalman filtering in the loosely coupled approach. Moreover, the restriction of visibility of at least four satellites is removed. We specifically use a tightly coupled GPS/reduced inertial sensor system approach.
Reduced Inertial Sensor System. Recently, microelectromechanical system or MEMS-grade inertial sensors have been introduced for low-cost navigation applications. However, these inexpensive sensors have complex error characteristics.
Therefore, current research is directed towards the utilization of fewer numbers of inertial sensors inside the inertial measurement unit (IMU) to obtain the navigation solution.
The advantage of this trend is twofold. The first is avoidance of the effect of inertial sensor errors. The second is reduction of the cost of the IMU in general. One such minimization approach, and the one used in our work, is known as the reduced inertial sensor system (RISS). The RISS configuration uses one gyroscope, two accelerometers, and a vehicle wheel-rotation sensor. The gyroscope is used to observe the changes in the vehicle’s orientation in the horizontal plane. The two accelerometers are used to obtain the pitch and roll angles. The wheel-rotation sensor readings provide the vehicle’s speed in the forward direction. FIGURE 2 shows a general view of the RISS configuration.
FIGURE 2. A general view of the RISS configuration.
A block diagram of the tightly coupled GPS/RISS used in our work is shown in FIGURE 3. At this stage, the system uses GPS pseudoranges together with the RISS observables to compute an integrated navigation solution. In this three-dimensional (3D) version of RISS, the system has a total of nine states. These states are the latitude, longitude, and altitude errors ( ; the east, north, and up velocity errors ; the azimuth error ; the error associated with odometer-driven acceleration ; and the gyroscope error .
The nine-state error vector xk at time tk is expressed as: (1)
FIGURE 3. Tightly coupled integration of GPS/RISS using differential pseudorange measurements.
Cycle Slip Detection and Correction
Cycle slip handling usually happens in two discrete steps: detection and fixing or correction. In the first step, using some testing quantity, the location (or time) of the slip is found. During the second step, the size of the slip is determined, which is needed along with its location to fix the cycle slip. Various techniques have been introduced by researchers to address the problem of cycle-slip detection and correction. Different measurements and their combinations are used including carrier phase minus code (using L1 or L2 measurements), carrier phase on L1 minus carrier phase on L2, Doppler (on L1 or L2), and time-differenced phases (using L1 or L2). In GPS/INS integration systems, the INS is used to predict the required variable to test for a cycle slip, which is usually the true receiver-to-satellite range in double-difference (DD) mode, differencing measurements between a reference receiver and the roving receiver and between satellites. In this article, we introduce a tightly coupled GPS/RISS approach for cycle-slip detection and correction, principally for land vehicle navigation using a relative-positioning technique.
Principle of the Algorithm. The proposed algorithm compares DD L1 carrier-phase measurements with estimated values derived from the output of the GPS/RISS system. In the case of a cycle slip, the measurements are corrected with the calculated difference. A general overview of the system is given in FIGURE 4.
FIGURE 4. The general flow diagram of the proposed algorithm.
The number of slipped cycles is given by (2)
where is the DD carrier-phase measurement (in cycles) is DD estimated carrier phase value (in cycles). is compared to a pre-defined threshold μ . If the threshold is exceeded, it indicates that there is a cycle slip in the DD carrier-phase measurements.
Theoretically, would be an integer but because of the errors in the measured carrier phase as well as errors in the estimations coming from the INS system, will be a real or floating-point number.
The estimated carrier-phase term in Equation (2) is obtained as follows: (3)
where λ is the wavelength of the signal carrier (in meters) are the estimated ranges from the rover to satellites i and j respectively (in meters) are known ranges from the base to satellites i and j respectively (in meters).
What we need to get from the integrated GPS/RISS system is the estimated range vector from the receiver to each available satellite ( ). Knowing our best position estimate, we can calculate ranges from the receiver to all available satellites through: (4)
where is the calculated range from the receiver to the mth satellite xKF is the receiver position obtained from GPS/RISS Kalman filter solution xm is the position of the mth satellite M is the number of available satellites.
Then, the estimated DD carrier-phase term in Equation (3) can be calculated and the following test quantity in Equation (2) can be applied: (5)
If a cycle slip occurred in the ith DD carrier-phase set, the corresponding set is instantly corrected for that slip by: (6)
where s is the DD carrier-phase-set number in which the cycle slip has occurred.
Experimental Work
The performance of the proposed algorithm was examined on the data collected from several real land-vehicle trajectories. A high-end tactical grade IMU was integrated with a survey-grade GPS receiver to provide the reference solution. This IMU uses three ring-laser gyroscopes and three accelerometers mounted orthogonally to measure angular rate and linear acceleration. The GPS receiver and the IMU were integrated in a commercial package. For the GPS/RISS solution, the same GPS receiver and a MEMS-grade IMU were used. This IMU is a six-degree of freedom inertial system, but data from only the vertical gyroscope, the forward accelerometer, and the transversal accelerometer was used. TABLE 1 gives the main characteristics of both IMUs. The odometer data was collected using a commercial data logger through an On-Board Diagnostics version II (OBD-II) interface. Another GPS receiver of the same type was used for the base station measurements. The GPS data was logged at 1 Hz.
Table 1. Characteristics of the MEMS and tactical grade IMUs.
Several road trajectories were driven using the above-described configuration. We have selected one of the trajectories, which covers several real-life scenarios encountered in a typical road journey, to show the performance of the proposed algorithm. The test was carried out in the city of Kingston, Ontario, Canada. The starting and end point of the trajectory was near a well-surveyed point at Fort Henry National Historic Site where the base station receiver was located. The length of the trajectory was about 30 minutes, and the total distance traveled was about 33 kilometers with a maximum baseline length of about 15 kilometers. The trajectory incorporated a portion of Highway 401 with a maximum speed limit of 100 kilometers per hour and suburban areas with a maximum speed limit of 80 kilometers per hour. It also included different scenarios including sharp turns, high speeds, and slopes.
FIGURE 5 shows measured carrier phases at the rover for the different satellites. Some satellites show very poor presence whereas some others are consistently available. Satellites elevation angles can be seen in FIGURE 6.
FIGURE 5. Measured carrier phase at the rover.FIGURE 6. Satellite elevation angles.
Results
We start by showing some results of carrier-phase estimation errors. Processing is done on what is considered to be a cycle-slip-free portion of the data set for some persistent satellites (usually with moderate to high elevation angles). Then we show results for the cycle-slip-detection process by artificially introducing cycle slips in different scenarios. In the ensuing discussion (including tables and figures), we show results indicating satellite numbers without any mention of reference satellites, which should be implicit as we are dealing with DD data.
FIGURE 7. DD-carrier-phase estimation error, reference satellite with PRN 22.FIGURE 8. Measured versus estimated DD carrier phase, reference satellite with PRN 22.
As can be seen in TABLE 2, the root-mean-square (RMS) error varies from 0.93 to 3.58 cycles with standard deviations from 0.85 to 2.47 cycles. Estimated phases are approximately identical to the measured ones. Nevertheless, most of the DD carrier-phase estimates have bias and general drift trends, which need some elaboration. In fact, the bias error can be the result of more than one cause. The low-cost inertial sensors always have bias in their characteristics, which plays a major role in this. The drift is further affecting relatively lower elevation angle satellites which can also be attributed to more than one reason. Indeed, one reason for choosing this specific trajectory, which was conducted in 2011, was to test the algorithm with severe ionospheric conditions as the year 2011 was close to a solar maximum: a period of peak solar activity in the approximately 11-year sunspot cycle.
Table 2. Estimation error for DD carrier phases (in cycles).
Moreover, the time of the test was in the afternoon, which has the maximum ionospheric effects during the day. Thus, most part of the drift trend must be coming from ionospheric effects as the rover is moving away from the base receiver during this portion of the trajectory. Furthermore, satellite geometry could contribute to this error component. Most of the sudden jumps coincide with, or follow, sharp vehicle turns and rapid tilts. Table 2 shows the averaged RMS and standard deviation (std) DD carrier-phase estimation error for the sample satellite-pairs. We introduced cycle slips at different rates or intensities and different sizes to simulate real-life scenarios. Fortunately, cycle slips are usually big as mentioned earlier and this was corroborated by our observations from real trajectory data. Therefore, it is more important to detect and correct for bigger slips in general.
Introducing and Detecting Cycle Slips. To test the robustness of the algorithm, we started with an adequate cycle slip size. Cycle slips of size 10–1000 cycles were introduced with different intensities. These intensities are categorized as few (1 slip per 100 epochs), moderate (10 slips per 100 epochs), and severe (100 slips per 100 epochs). This was applied for all DD carrier-phase measurement sets simultaneously. The threshold was set to 1.9267 (average of RMS error for all satellite-pairs) cycles. Four metrics were used to describe the results. Mean square error (MSE); accuracy, the detected cycle slip size with respect to the introduced size; True detection (TD) ratio; and Mis-detection (MD) ratio. Due to space constraints and the similarity between results for different satellites, we only show results for the reference satellite with PRN 22. FIGURES 9–12 show introduced versus calculated cycle slips along with the corresponding detection error for sample satellites in the different scenarios. TABLES 3–5 summarize these results.
FIGURE 9. Introduced and calculated cycle slips (upper plot) and detection error (lower plot). Few cycle slips case, reference satellite with PRN 22.FIGURE 10. Introduced and calculated cycle slips (upper plot) and detection error (lower plot). Moderate cycle slips case, reference satellite with PRN 22.FIGURE 11. Introduced and calculated cycle slips (upper plot) and detection error (lower plot). Intensive cycle slips case, reference satellite with PRN 22.FIGURE 12. Introduced and calculated cycle slips (upper plot) and detection error (lower plot). Small cycle slips case, reference satellite with PRN 22.Table 3. Few slips (1 slip per 100 epochs).Table 4. Moderate slips (10 slips per 100 epochs).Table 5. Intensive slips (100 slips per 100 epochs).
All introduced cycle slips were successfully detected in all of the few, moderate, and severe cases with very high accuracy. A slight change in the accuracy (increasing with higher intensity) among the different scenarios shows that detection accuracy is not affected by cycle-slip intensity. Higher mis-detection ratios for smaller cycle-slip intensity comes from bigger error margins than the threshold for several satellite pairs. However, this is not affecting the overall accuracy strongly as all mis-detected slips are of comparably very small sizes. MD ratio is zero in the intensive cycle-slip case as all epochs contain slips is an indicator of performance compromise with slip intensity.
It is less likely to have very small cycle slips (such as 1 to 2 cycles) in the data and usually it will be hidden with the higher noise levels in kinematic navigation with low-cost equipment. However, we wanted to show the accuracy of detection in this case. We chose the moderate cycle slip intensity for this test. TABLE 6 summarizes results for all satellites.
Table 6. Small slips (1–2 cycles) at moderate intensity (10 slips per 100 epochs).
We get a moderate detection ratio and modest accuracy as the slips are of sizes close to the threshold. The MSE values are not far away from the case of big cycle slips but with higher mis-detection ratio.
Conclusions
The performance of the proposed algorithm was examined on several real-life land vehicle trajectories, which included various driving scenarios including high and slow speeds, sudden accelerations, sharp turns and steep slopes. The road testing was designed to demonstrate the effectiveness of the proposed algorithm in different scenarios such as intensive and variable-sized cycle slips.
Results of testing the proposed method showed competitive detection rates and accuracies comparable to existing algorithms that use full MEMS IMUs. Thus with a lower cost GPS/RISS integrated system, we were able to obtain a reliable phase-measurement-based navigation solution. Although the testing discussed in this article involved post-processing of the actual collected data at the reference station and the rover, the procedure has been designed to work in real time where the measurements made at the reference station are transmitted to the rover via a radio link. This research has a direct influence on navigation in real-time applications where frequent cycle slips occur and resolving integer ambiguities is not affordable because of time and computational reasons and where system cost is an important factor.
Acknowledgments
This article is based on the paper “Real-time Cycle-slip Detection and Correction for Land Vehicle Navigation using Inertial Aiding” presented at ION GNSS+ 2013, the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation held in Nashville, Tennessee, September 16–20, 2013.
Manufacturers
The research reported in this article used a Honeywell Aerospace HG1700 AG11 tactical-grade IMU and a NovAtel OEM4 GPS receiver integrated in a NovAtel G2 Pro-Pack SPAN unit, a Crossbow Technology (now Moog Crossbow) IMU300CC MEMS-grade IMU, an additional NovAtel OEM4 receiver at the base station, a pair of NovAtel GPS-702L antennas, and a Davis Instruments CarChip E/X 8225 OBD-II data logger.
Malek Karaim is a Ph.D. student in the Department of Electrical and Computer Engineering of Queen’s University, Kingston, Ontario, Canada.
Tashfeen Karamat is a doctoral candidate in the Department of Electrical and Computer Engineering at Queen’s University.
Aboelmagd Noureldin is a cross-appointment professor in the Departments of Electrical and Computer Engineering at both Queen’s University and the Royal Military College (RMC) of Canada, also in Kingston.
Mohamed Tamazin is a Ph.D. student in the Department of Electrical and Computer Engineering at Queen’s University and a member of the Queen’s/RMC NavINST Laboratory.
Mohamed M. Atia is a research associate and deputy director of the Queen’s/RMC NavINST Laboratory.
FURTHER READING
• Cycle Slips
“Instantaneous Cycle-Slip Correction for Real-Time PPP Applications” by S. Banville and R.B. Langley in Navigation, Vol. 57, No. 4, Winter 2010–2011, pp. 325–334.
“GPS Cycle Slip Detection and Correction Based on High Order Difference and Lagrange Interpolation” by H. Hu and L. Fang in Proceedings of PEITS 2009, the 2nd International Conference on Power Electronics and Intelligent Transportation System, Shenzhen, China, December 19–20, 2009, Vol. 1, pp. 384–387, doi: 10.1109/PEITS.2009.5406991.
“Cycle Slip Detection and Fixing by MEMS-IMU/GPS Integration for Mobile Environment RTK-GPS” by T. Takasu and A. Yasuda 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. 64–71.
“Cycle-Slip Detection and Repair in Integrated Navigation Systems” by A. Lipp and X. Gu in Proceedings of PLANS 1994, the IEEE Position Location and Navigation Symposium, Las Vegas, Nevada, April 11–15, 1994, pp. 681–688, doi: 10.1109/PLANS.1994.303377.
Short-Arc Orbit Improvement for GPS Satellites by D. Parrot, M.Sc.E. thesis, Department of Geodesy and Geomatics Engineering Technical Report No. 143, University of New Brunswick, Fredericton, New Brunswick, Canada, June 1989.
• Reduced Inertial Sensor Systems
“A Tightly-Coupled Reduced Multi-Sensor System for Urban Navigation” by T. Karamat, J. Georgy, U. Iqbal, and N. Aboelmagd 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. Okou, and N. Aboelmagd in Proceedings of PLANS 2008, the IEEE/ION Position Location and Navigation Symposium, Monterey, California, May 5–8, 2008, pp. 1014–1021, doi: 10.1109/PLANS.2008.4570075.
• Integrating GPS and Inertial Systems
Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration by N. Aboelmagd, T. B. Karmat, and J. Georgy. Published by Springer-Verlag, New York, New York, 2013.
Aided Navigation: GPS with High Rate Sensors by J. A. Farrell. Published by McGraw-Hill, New York, New York, 2008.
Global Positioning Systems, Inertial Navigation, and Integration, 2nd edition, by M.S. Grewal, L.R. Weill, and A.P. Andrews. Published by John Wiley & Sons, Inc., Hoboken, New Jersey, 2007.
Averna has issued a new software release for the DP-360 DOCSIS Protocol Analyzer, featuring support for 16×4 channel bonding for broadband testing.
Averna’s DP-360 provides functional DOCSIS and EuroDOCSIS network analysis, allowing for exceptional visibility into all layers of the network, the company said. Multiple system operators (MSOs), chipset manufacturers, product developers and certification bodies use the DP-360 to quickly find and correct trouble spots.
New DP-360 release highlights:
Supports up to 16 single or bonded downstream channels for testing 16×4 configuration.
Upstream gain control, MER reading and power reading available in the remote API for automated power adjustment.
Automatic detection of modulation type (64-QAM or 256-QAM) and DOCSIS on downstream channels and lock on 4 upstream frequencies for faster setup and analysis.
Support for DOCSIS 3.0 Energy Management messages (EM-REQ & EM-RSP).
DP-360 clients also have access to Averna’s new DOCSIS 3.1 Early Adopter Program, which offers MSOs and equipment vendors a smooth and cost-effective transition to Averna’s next-generation solution for testing their cable equipment based on the new DOCSIS 3.1 standard. Contact us for more details.
“Our DOCSIS 3.1 Early Adopter Program covers current D3.0 as well as upcoming D3.1 testing needs and is specifically designed to make the technology switch as easy and cost effective as possible for our clients and partners,” commented Alex Pelland, Director of Broadband Test Strategy for Averna. “The DP-360 is the most advanced DOCSIS protocol analyzer available today and, with our forward-looking transition program, it will provide a substantial return on investment for years to come.”
The new DP-360 software release is available at no cost to customers with a valid DP-360 maintenance and support agreement.
Aircraft position as obtained by Galileo-only receiver during Netherlands flight.
The European Space Agency’s Galileo satellites have achieved their first aerial fix of longitude, latitude and altitude, enabling the inflight tracking of a test aircraft. ESA’s four Galileo satellites in orbit have supported months of positioning tests on the ground across Europe since the first fix in March.
Now the first aerial tracking using Galileo has taken place, marking the first time that Europe has been able to determine the position of an aircraft using only its own independent navigation system. The milestone took place on a Fairchild Metro-II above Gilze-Rijen Air Force Base in the Netherlands at 12:38 GMT on November 12. It was part of an aerial campaign overseen jointly by ESA and the National Aerospace Laboratory of the Netherlands, NLR, with the support of Eurocontrol, the European Organisation for the Safety of Air Navigation, and LVNL, the Dutch Air Navigation Service Provider.
A pair of Galileo test receivers was used aboard the aircraft, the same kind employed for Galileo testing in the field and in labs across Europe. They were connected to an aeronautical-certified triple-frequency Galileo-ready antenna mounted on top of the aircraft.
Fairchild Metro-II aircraft used for Galileo airborne testing.
Tests were scheduled during periods when all four Galileo satellites were visible in the sky – four being the minimum needed for positioning fixes. The receivers fixed the plane’s position and, as well as determining key variables such as the position, velocity and timing accuracy; time to first fix; signal-to-noise ratio; range error; and range–rate error.
Testing covered both Galileo’s publicly available Open Service and the more precise, encrypted Public Regulated Service, whose availability is limited to governmental entities.
Flights covered all major phases: take off, straight and level flight with constant speed, orbit, straight and level flight with alternating speeds, turns with a maximum bank angle of 60º, pull-ups and push-overs, approaches and landings.
They also allowed positioning to be carried out during a wide variety of conditions, such as vibrations, speeds up to 456 km/h, accelerations up to 2 ghorizontal and 0.5–1.5 gvertical, and rapid jerks. The maximum altitude reached during the flights were 3000 m.
NLR’s Fairchild Metro-II has previously performed initial European GPS testing in the 1980s, and the first tests of the European Geostationary Navigation Overlay Service, EGNOS, which sharpens GPS accuracy and monitors its reliability over Europe for high-accuracy or even safety-of-life uses.
The definition and development of Galileo’s in-orbit validation phase were carried out by ESA and co-funded by ESA and the EU.
The Full Operational Capability phase is managed and fully funded by the European Commission. The Commission and ESA have signed a delegation agreement by which ESA acts as design and procurement agent on behalf of the Commission.
Rohde & Schwarz extends the functionality of the R&S SMBV100A vector signal generator by adding BeiDou/Compass capability to its integrated GNSS simulator. With the R&S SMBV-K107 option, the GNSS simulator now covers the BeiDou standard as well as the GPS, Galileo and GLONASS satellite navigation systems.
The new option allows users to generate real-time scenarios with up to 24 BeiDou satellites. R&S SMBV-K107 supports all possible BeiDou orbits and can therefore even simulate satellites that are not yet in orbit. It also supports hybrid scenarios with GPS, Galileo or GLONASS satellites. A software update makes it easy to upgrade existing GNSS simulators for BeiDou. No hardware modifications are required.
The R&S SMBV100A permits users to quickly define their own satellite scenarios to test GNSS receivers under diverse conditions. A wide range of options are available for simulating realistic effects such as signal obscuration and multipath propagation. These scenarios can now be configured for BeiDou as well.
This inexpensive solution is one of the few on the market that does not require an external PC for testing receivers and components of satellite-based navigations systems, the company said. In addition to GNSS signals, the R&S SMBV100A can simulate mobile radio, wireless and radio standards, allowing users to test several functions with a single instrument.
The new R&S SMBV-K107 option is now available from Rohde & Schwarz.
Analysis of Signal Tracking Techniques for Multipath Mitigation
By Antonio Fernández, Mariano Wis, Pau Closas, Carles Fernández-Prades, José A. García, Francesca Zanier, and Massimo Crisci
INNOVATION INSIGHTS by Richard Langley
GETTING RID OF A NUISANCE. No, I’m not talking about your neighbor’s barking dog or the IT guy when he shows up to fiddle, yet again, with your computer. I’m talking about multipath. What is multipath, you ask? Herewith, Multipath 101. When a radio signal travels from a transmitting antenna to a receiving antenna, it will follow a direct line-of-sight path. But the signal might also travel to the receiving antenna after being reflected off a nearby building, say, resulting in a delayed signal or echo along with the line-of-sight one. Those of us of a certain age will remember ghost images on the screens of TVs connected to “rabbit ears” or outdoor antennas. That was multipath. These days, with TV signals primarily delivered by cable and satellite, we don’t see multipath much anymore. But we do hear it in our cars, from time to time, while listening to FM radio. Although the FM “capture effect” provides some margin against multipath, it is not uncommon to lose stereo reception or to experience fading out of the signal while driving in built-up areas as a result of reflections.
This same multipath phenomenon also affects GNSS signals. Unlike satellite TV antennas, the antennas feeding our GNSS receivers are omnidirectional. So we have the possibility of not only receiving a direct, line-of-sight signal from a GNSS satellite but also any indirect signal from the satellite that gets reflected off nearby buildings or other objects or even the ground. The related phenomena of diffraction and scattering can also generate multipath signals.
In a GNSS receiver, the line-of-sight and multipath signals combine to corrupt tracking of the line-of-sight signal resulting in increased pseudorange and carrier-phase measurement errors.
GNSS antenna and receiver manufacturers have developed techniques to minimize some of the impact of multipath on the GNSS observables. And tracking of some of the newer GNSS signals is a bit more resistant to multipath. But multipath, at some level, is still a problem looking for a better solution.
This brings us to ARTEMISA, which stands for Advanced Receiver Techniques: Multiprocessing Algorithms. It’s a European initiative to develop techniques to minimize the effects of multipath in GNSS receivers. For those of you who are a little rusty on your Greek mythology, Artemis (or Artemisa in Spanish — after all, she was a woman) was the Greek goddess of the hunt. You might better know her Roman equivalent: Diana. Her parents were Zeus and Leto, and Apollo was her twin brother. She is often depicted carrying a bow and arrows. How appropriate a name for a project whose goal is to try to kill off the effects of multipath in GNSS receivers.
In this month’s column, the team of researchers involved with ARTEMISA describe their efforts to generate synthetic multipath for GPS L1 and Galileo E1 signals and to test different signal tracking techniques in a simulated receiver to see which techniques best minimize the effects of multipath on positioning solutions and which might be feasible candidates for incorporating in real receivers. The hunt is on.
GNSS navigation in urban environments is usually challenged by a number of effects such as multipath and weak signal conditions. In particular, the pernicious effects of multipath on signal tracking and system accuracy are widely known. To mitigate these effects, there is a series of techniques that range from modified antenna design to combining the GNSS receiver with other sensors or subsystems. Another possibility is to implement advanced tracking techniques specifically designed for these purposes. Such techniques usually impose computational load and implementation complexity, which make them hard to implement in an application-specific-integrated-circuit-based receiver. However, given the current advances in computer technology and the possibilities of field-programmable-gate-array- (FPGA-)based hardware, it is possible to implement these new techniques in an operational receiver.
We have studied this possibility as part of the ARTEMISA project, carried out by DEIMOS Space and the Centre Tecnològic de Telecomunicacions de Catalunya, and supervised by the European Space Agency’s European Space Research and Technology Centre. We have implemented and tested a series of innovative techniques that are able to cope with, and even to estimate, multipath (MP) parameters, using a simulated software receiver based on the GRANADA (Galileo Receiver Analysis and Design Application) GNSS blockset for MathWork’s Simulink graphical programming language tool. These techniques are based on the maximum likelihood principle (as implemented in the Multipath Estimating Delay Lock Loop) or on online Bayesian techniques for the estimation of multipath (as implemented in the Multipath Estimating Particle Filter), involving architectural modifications of the tracking loops (as in vector tracking loops), or even constituting a new paradigm in receiver design (direct position estimation).
Our effort in this project has focused on two main tasks. The first task is the design and development of the simulation platform, the techniques to be tested, and a multipath model representative of the urban environment. The second task involves a simulation campaign that has been carried out to test the different techniques and to contrast the results obtained against the legacy delay lock loop / phase lock loop (DLL/PLL) tracking loop schemes. This article describes these tasks and some of the results we have obtained so far.
Keep in mind that at the time of writing, ARTEMISA is still ongoing. Therefore, more results are expected up until the end of the project.
Simulation Platform
The simulation platform has been developed in Matlab/Simulink with DEIMOS Engenharia’s GRANADA GNSS Blockset. This blockset is a collection of Simulink models and blocks that can be used to design and simulate any kind of GNSS receiver. The main block is the Factor Correlator Model (FCM), which implements the set of correlators through an analytical (set of equations) model. Carrier phase, code misalignment, autocorrelation function, and even the correlated noise and the multipath at the output of every virtual correlator are simulated for a given input trajectory. On the other hand, the tracking loops are implemented as independent modules representative of an actual receiver. This semi-analytical approach has the advantage of performing a fast simulation of the correlator output without the need for implementing the baseband correlation operation. In addition, its implementation in Simulink allows for the development of innovative tracking schemes. This approach also matches with the ARTEMISA project concept, where a series of innovative tracking loops has been implemented with the aim of replacing or improving conventional PLL/DLL schemes.
The main architecture of the simulation platform is shown in FIGURE 1. We use an external trajectory file to generate the reference data that will be used with the FCM block to generate the correlator output that will feed the tracking loop blocks. These trajectory files are also used to feed the multipath scenario generators, to test each technique under a number of defined scenarios so that we can assess their performances and find their limitations under multipath.
We used two statistical models for the description of the signal propagation: the Controlled Stochastic Channel Model (CSCM), which is a modification of the Land Mobile Satellite channel developed by Pérez-Fontán, and the well-known Land Mobile Channel Model, developed by the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt or DLR); see Further Reading. These models generate a series of scenario files, which can be loaded into the FCM to introduce the multipath effects in the correlator output.
These two statistical models are complementary. The CSCM model allows the user to set the multipath channel characteristics to be able to stress the tracking technique, while the DLR model permits evaluation of the technique’s response in realistic conditions.
A deterministic user-defined multipath generator was implemented to check the response of the tracking techniques under well-defined multipath conditions. The Multipath Error Envelope (MPEE) was also computed to evaluate the response of the technique under one echo with varying delay conditions.
The purpose of this article is not to explain all the developments performed with the platform. It focuses on the results obtained with the deterministic and statistical channel model. For this reason, the development of the CSCM generator will be explained in detail.
Controlled Stochastic Channel Model
The CSCM module was specifically created for this project. Its purpose is to generate a stochastic channel, but with the capability to control the multipath power levels and the number of echoes generated in the scenario, thus creating a set of realistic multipath signals, but with the capability of being easily controlled by the user. Time series for multipath echoes are generated, following a Rice or Rayleigh stochastic model, but the mean power levels, the amplification K factors, and the power switching times are chosen by the user.
The model allows the selection of the number of satellites (channels) that are generated in the model, the sampling period (related to the loop integration time), the length of the simulation, the receiver speed, the signal carrier frequency, and channel specific parameters such as the number of echoes, the average delay of the echoes and the decay slope (echo power loss ratio with signal delay).
One of the parameters the user can set implicitly is the multipath echo lifetime. What the user really sets is the channel transition time, or the time during which the channel keeps the multipath configuration. Every time the transition time is changed, a series of multipath echoes are canceled and other ones appear. This set of disappearing/appearing echoes is performed in pairs in such a way that the transition between echoes is smooth. FIGURE 2 illustrates this mechanism.
Figure 2. CSCM echoes smooth transition.
When an echo is disappearing (a red one in the figure), its associated echo is at its maximum value (a blue scatterer). In the next interval, a new echo appears in a different delay position (a green one) and the associated scatterer begins to decrease its power. This mechanism allows the user to easily associate the transition time to half the multipath echo lifetime.
Simulation Plan
The simulation plan is structured into different stages. The first stage of the simulation plan is based on the controlled multipath environment, with specific tests for each technique. The purpose of this stage is the tuning up of the techniques. As an example, for the Multipath Estimating Delay Lock Loop (MEDLL) technique, parameters such as the precorrelation bandwidth, the number of MEDLL iterations or the number of assumed multipath echoes are parameters that are adjusted after these tests are carried out. Another purpose of this first stage is to check the performance of the techniques under deterministic, fully controlled multipath. Parameters like signal-to-multipath ratio, multipath delay, and the number of multipath echoes can be controlled at any time.
Another test is the generation of the multipath envelope error plot. The utility of this test is to find the optimal configuration of the correlator position for each kind of signal, which is a trade-off between the obtained root-mean-square error (RMSE) and the number of correlators. This procedure is repeated for each signal considered in the plan.
The next stage of the simulation plan is the test with the CSCM model. This test consists of the generation of a series of scenarios characterized by the stochastic model, the number of echoes, and the user receiver dynamics. Scenarios presented in this article are shown in TABLE 1. Two types of user environments are defined: one for pedestrian and one for vehicular receivers. The dynamics (user speed) define the integration (sampling time) and the multipath Doppler spread. The stochastic model parameters include the type of stochastic model for the line-of-sight signal (LOSS) and multipath echoes, mean power level, and amplification K factor. These scenarios represent a moderate multipath level with four echoes.
Table 1. CSCM scenarios in the simulation plan.
Each scenario was run with different settings of carrier-to-noise-density ratio (C/N0) and with all the signals that were planned for the project: GPS L1 C/A, GPS L5, Galileo E1 and Galileo E5. In this article, we focus on L1 band signals, analyzing the results for GPS L1 and Galileo E1.
Table 2. GNSS signals considered in the analysis.
The final stage in the simulation plan is testing each technique under realistic channel conditions with the DLR model. In this case, an urban scenario is set, assuming two types of receiver dynamics (a pedestrian user and a vehicular user). No more details about this stage are given because these tests are currently ongoing.
Technique Descriptions
After an initial evaluation of the candidate techniques, the following techniques were selected for implementation and testing in the project.
Multipath Estimating Delay Lock Loop. MEDLL was proposed by Van Nee (see Further Reading). It is a robust statistical approach to the multipath problem, where the maximum likelihood principle is applied to a signal model consisting of LOSS and M-1 multipath rays or echoes. The key idea is to perform the estimation of the whole set of parameters of the incoming signals (that is, amplitude, delay, and phase) in an iterative manner. When their amplitudes, time delays, and carrier phases are estimated, the effect of the reflections in the correlation can be removed. Applying standard assumptions, the maximization of the likelihood function yields a set of interrelated equations from which one can estimate iteratively the LOSS and multipath parameters. The implementation of MEDLL considers a similar architecture as conventional DLLs, although in practice more than three correlators per loop are required for effective multipath mitigation. Despite its demanding requirements in terms of a large number of correlators and computational load, MEDLL was successfully implemented in NovAtel receivers because of its excellent performance in multipath mitigation.
The contributions of all signals (that is, LOSS and an unknown number of echoes) are not calculated simultaneously from the outset. First, the contribution of only one signal is calculated, then the contribution of a second signal is added with the contributions of both signals being optimized, then the contribution of a third signal is added and the contributions of all three signals are optimized, and so on. The process is repeated until a suitable stop criterion is fulfilled. A possible approach for deciding when to stop adding more rays to the signal model is to detect when the error increases; that is, observing that the signal-to-residual ratio when considering an extra path decreases with respect to the case of not considering it, or reaching a maximum number of considered paths, which is a design parameter of this technique.
Multipath Estimating Particle Filter. The MEPF shares with MEDLL the philosophy of estimating the parameters of multipath to mitigate its effect. The main difference is that in this case the statistical principle is not the ML (as in MEDLL) but Bayesian filtering. Here the term Bayesian means that the algorithm is using some sort of a priori information regarding these parameters (such as interdependencies and time evolution models). Therefore, instead of assuming that each integration period is independent of the others, a first-order Markov process is assumed for the unknown parameters (that is, amplitude, delay, and phase). The resulting problem is formulated as a nonlinear state-space model and can be solved by means of a Rao-Blackwellized particle filtering method.
The MEPF has a relatively high computational load, which is a function of the number of particles (Np), the number of correlation points, and the number of rays to be estimated (M). In all cases, the larger the values the more computationally demanding the algorithm is. According to the simulations performed, configurations with Np larger than 2000 are not worthwhile, because it implies a high computational cost and the results do not improve significantly the accuracy of a GNSS receiver. Also notice that the dimension of the state-space to be estimated is 3 × M ([amplitude, phase, delay] × M), and thus estimating an additional multipath ray implies augmenting the dimensionality by three. Theoretically, the convergence results of particle filters are independent of the dimensionality of the problem. However, it is agreed that in practice these methods fail when the problem increases in dimension. Therefore, setting M larger than 5 is not reasonable since the number of particles required for convergence would be too large to consider its implementation in a receiver.
Vector Tracking Loops. A conventional GNSS receiver consists of several parallel scalar DLLs, each of which independently estimates the individual pseudoranges. The parallel set of measured pseudoranges (plus Doppler or accumulated delta range measurements on the carrier) are then fed to a Kalman filter estimator, and thus each DLL effectively produces an independent estimate for each of the N pseudoranges for each of the N satellites. However, not all of their measurements are truly independent, although they are treated as such by the DLL, and if there are more than four measurements being made and four or fewer unknowns, the system is overdetermined. Furthermore, the geometry of the satellite-user paths generally prevents the measurements from being truly independent.
The concept of vector tracking loops firstly appeared in the early 1980s. Recently, the method has attracted the attention of many researchers (see Further Reading) due to its good performance in weak signal scenarios.
In this article, we present the implementation of a vector tracking loop that works with pseudorange and pseudorange-rate measurements and provides estimations for position, velocity, receiver clock error, and receiver clock drift. The vector loop has been integrated in a basic receiver based on the classical DLL and PLL/frequency lock loop (FLL)-assisted architecture, acting as an overlay procedure, which activates automatically once the basic receiver has obtained the first position fix. Then, the position/velocity/time solution is used to jointly estimate the synchronization parameters (time delay and carrier phase) for each of the received GNSS signals by means of an extended Kalman filter, and those values are re-injected into the tracking loop. By using position for deriving such synchronization parameters, the algorithm exploits the problem’s inherent geometric constraints, processing all the channels jointly and providing robustness in scenarios with weak receiving power, high multipath, or fast fading.
Direct Position Estimation. Although the conventional two-step position determination is the approach taken traditionally, it is seen to have a number of drawbacks. In contrast, direct position estimation (DPE for short) proposes an alternative where the estimation of a user’s position is performed directly from the received and sampled signal, thus avoiding intermediate steps and jointly considering signals from all satellites when estimating the position solution. By merging the two-step approach into a single estimation problem, DPE addresses some of the inherent drawbacks of the conventional approach where the dependencies between channels are efficiently exploited, in the sense that signals from visible satellites are jointly processed to obtain the user’s position. At the time of writing, this technique is being implemented and its analysis is left for future publications.
Results
Not all the results that we have obtained have been included in this article; only the most relevant ones for L1/E1 CBOC and BPSK signals are given.
MEDLL. Optimal Correlator Configuration. As stated previously, the test consisted of executing different correlator configurations. TABLE 3 shows the correlator configuration ID, the number of correlation samples, and the location of the late correlators with respect to the prompt one and normalized to the chip time (Tc). The corresponding early samples are defined analogously. For all these configurations, it is assumed that there is a correlator at zero chips.
Table 3. MEDLL correlator configurations tested.
These configurations were selected in order to test different possibilities as regular spacing between correlators (MP31, MP61, or MP91), setting the correlators to inflexion points of the autocorrelation function (ACF) (NC37 or NC43 for CBOC modulation), or variable spacing (as configurations AR01 to AR05 and GE01) that optimize the number of correlators. The results of the tests can be observed in FIGURE 3 for a BPSK(1) signal and for a CBOC(6,1,1/11) signal.
Figure 3. MPEE for MEDLL with BPSK and CBOC signals for different correlator configurations (Tc=chip time).
In general, it is observed that the greater the number of correlators, the smaller is the spacing and therefore, the area covered by the multipath error envelope is smaller. However, the greater the number of correlators, the greater is the processing time. Therefore, it is necessary to find an optimal configuration that best fits the multipath variation.
After having analyzed the tests for these configurations, we found that the optimal configurations are AR01 for the BPSK signal and NC37 for the CBOC(6,1,1/11) signal. These configurations are shown in FIGUR 4, and were used for the remaining tests.
Figure 4. Correlator configurations for CBOC and BPSK signals.
CSCM. As mentioned in the simulation plan description, the tests for a pedestrian and a vehicular user in a moderate multipath environment were performed with a scenario file generated with the CSCM tool. These scenarios are characterized by a series of multipath echo levels, number of echoes, and specific stochastic models that are detailed in Table 1. In this table, the pedestrian scenario is known as SP1 and the vehicular scenario is shown as SV1. The results with these scenarios are presented in this article.
The RMSE for range estimates in these scenarios is shown in FIGURE 5 for SP1 and SV1. For comparison, these plots show also the theoretical lower bound (Nunes bound) computed for each technique.
Figure 5. Range RMSE for MEDLL in pedestrian and vehicular scenarios.
The plots show that MEDLL outperforms the conventional DLL results above a minimum C/N0 value. This happens beyond 32 dB-Hz for a CBOC signal and 35 dB-Hz for a BPSK(1) signal for low dynamics (pedestrian) environments.
In the case of vehicle scenarios such as SV1, it is observed that it requires significantly higher values of C/N0 in order to outperform the DLL RMSE. This result makes the technique impractical for vehicular applications.
Time Performance. A collateral result that has been obtained with CSCM scenarios is the time performance of the technique, compared to the legacy DLL/PLL scheme. The ratios of the execution times needed for the techniques have been computed. This is an indicative measure of the computational load. The time performance of MEDLL with the selected correlator configuration against DLL is 10:1. That is, 10 times more time is required for the MEDLL technique than the DLL to run the same scenario. It is also observed that the ratio for BPSK modulation is slightly larger than the ratio for the CBOC signal, because more correlators are used in that case.
MEPF. Performance Under Controlled Channel. FIGURE 6 shows an example of the performance of the MEPF technique tested in a controlled channel environment.
Figure 6. Results of the MEPF in the controlled multipath environment with 2 and 3 estimated rays including the line-of-sight. For comparison purposes, DLL/PLL results are also included (blue line in upper plot).
It can be observed how the DLL/PLL technique has an error, which grows as the number of rays is increased. However, when the MEPF is applied, it can be observed how the technique is capable of dealing with a multipath-changing environment despite the fact that it is varying in time with the number of rays increasing. It can also be noticed that the response of the technique is practically the same independent of the number of estimated rays (M). These results were achieved with a BPSK signal and 500 particles.
Covariance Matrix Adjustment. Before starting the specific test for the evaluation of the scenario performance, it is necessary to calibrate the particle filter. The calibration procedure consists of the adjustment of the process covariance matrix. The observation covariance can be adjusted, simply by analyzing the raw observables, or it can even be done automatically.
The critical point is the adjustment of the states’ covariance. It has been observed that an improper adjustment of the covariance may cause an increment in the range RMSE or even the divergence of the filter. For that reason, a systematic procedure for the adjustment of the covariance matrix has been followed. This procedure evaluates the performance using different configurations of the covariance matrices of the line-of-sight and multipath parameters in two stages, and allows us to find a set of optimal values for each scenario.
Optimal Correlator Configuration. We also analyzed the optimal correlator configuration for the MEPF technique. A number of configurations in Table 3 were used under CSCM scenarios. It was found that the correlator configuration does not have a strong influence on the performance of the MEPF technique. It is worth mentioning that in cases where fewer than five correlators were used, the performance degraded. Despite the weak influence of the range RMSE with the correlator configuration, it was observed that there is a minimum for the BPSK signal that can be chosen for the MEPF technique (MP31). For the CBOC signal, the same configuration used for MEDLL is finally chosen (NC37), provided that it is the optimal configuration that balances range RMSE and the number of correlators.
Number of Particles. It is important to notice that an initial set of tests was performed with 500 particles. However, it was found that by increasing the number of particles to 2000, the performance of the technique with CSCM was remarkably better. Because of this, for the remaining tests using the CSCM, this number of particles was the default value. Using a higher number is not worthwhile since results are not much improved and the computational load becomes too high.
CSCM Results. The range RMSE results for the pedestrian scenario SP1 with the MEPF technique are shown in FIGURE 7. In this case, it is observed that for the CBOC signal, MEPF outperforms the DLL/PLL technique for low C/N0 values. This result could not be reproduced for the BPSK signal because of the adjustment of the covariance matrix, which in this test was optimized for CBOC.
Figure 7. Range RMSE for MEPF and pedestrian scenario.
These promising results for CBOC open the possibility of using this technique in applications where the LOSS is very weak or where the multipath signals are very strong. More tests with this technique are currently being executed for a better adjustment of covariance settings for BPSK.
Time Performance. The previous time performance analysis was performed with the MEPF technique. The performance ranges among 180:1 and 340:1, when compared to DLL/PLL schemes using three correlation samples. The extremely long time required to simulate the scenario makes this technique inadvisable for implementation within an operational receiver using current technology.
VTL.CSCM Results. The same CSCM scenarios were performed for the VTL technique, but using a multi-channel receiver. Results for pedestrian SP1 and vehicular SV1 scenarios are shown in FIGURE 8.
Figure 8. Position RMSE for VTL with pedestrian and vehicular scenarios.
It must be noted that, in order to perform fair comparisons, the results of ordinary DLL/PLL tracking used a conventional KF for the computation of the position instead of the least squares module available in the GRANADA GNSS Blockset.
In our numerical simulations, a significant improvement in the performance of VTL with respect to the DLL/PLL+KF scheme was not observed in pedestrian environments, due to the low dynamics. Under those dynamic conditions, both systems exhibited similar (statistically equivalent) behavior.
In the case of scenario SV1, the velocity applied to the receiver was higher than in the pedestrian case, and the VTL exhibited a remarkable improvement over the DLL/PLL+KF-based receiver. For the BPSK modulation, it was observed that for low values of C/N0 , the VTL performs better than DLL/PLL+KF. However, for stronger signals, both techniques have the same behavior as shown for the pedestrian scenario results. On the other hand, for the CBOC modulation, it is observed that VTL performs better than DLL/PLL+KF for the whole range of C/N0 values. The precorrelation bandwidth was set to 8 MHz in the case of BPSK, while for CBOC it was set to 14 MHz. That wider bandwidth of the CBOC receiver has an impact in the estimation of the measurements covariance matrix. In can be observed that in such higher dynamic stress conditions, the VTL outperformed DLL/PLL+KF in our numerical simulations.
It must be mentioned that for all the simulations, we assumed the same C/N0 for all satellites. However, we plan to run tests with LOSS fading (variation of C/N0). It is expected that the VTL technique will show its advantage in those simulations.
It was also observed that settings of the KF covariance have an important effect on the results. This covariance must be adjusted according to the multipath signal level. Therefore, a calibration operation, which adapts the KF to a particular scenario should be performed. While the measurement covariance matrix can be adaptively estimated from the output of the DLLs and the PLLs, the process covariance matrix should be adjusted depending on the trajectory characteristics.
Time Performance. The time performance analysis also shows that the time performances of VTL and DLL/PLL+KF are very similar. Only a small increment of 15% of the computational cost for vehicular scenarios has been observed. This makes this technique a good candidate to be implemented in an operational software-based receiver.
Results Overview
After analyzing the current results from the different techniques, some general conclusions on their performance can be made.
MEDLL can be useful to mitigate and improve the pseudorange measurement provided that the C/N0 value is greater than a threshold value. An intuitive reasoning for this is that the estimation of multipath rays is easier at high C/N0 values. Below this value, the MEDLL technique does not present an important advantage over ordinary DLL/PLL given the time performance it has (a ratio of 10:1). MEDLL is especially well-suited for static and pedestrian environments.
MEPF is a technique that still needs research before it can be used operatively. Results show that it works quite well for low C/N0 values when compared to DLL/PLL. Results show that for the CBOC signal, at least 2000 particles are needed to give good results for low C/N0 values. However, its time performance is very poor (around 200:1 with respect to DLL with 2000 particles)
VTL does not present an advantage in the pseudorange measurement domain, but it clearly improves the position solution with respect to a classical DLL/PLL+KF tracker. This improvement is remarkable under dynamic conditions. It is also observed that the time performance is very similar to the DLL/PLL+KF one. Therefore this technique is a good candidate for implementation in a receiver prototype based on embedded hardware, an FPGA implementation, or software-based radio.
For the sake of completeness, a performance comparison in the range domain of the different techniques with the BPSK and CBOC signals for the pedestrian SP1 scenario are presented in FIGURE 9. The metric used is the range RMSE.
Figure 9. Compared range RMSE for different techniques: pedestrian scenario with BPSK(1) and CBOC(6,1,1/11) signals.
Conclusions and Future Work
This article has presented a series of innovative multipath-estimating techniques using non-conventional approaches in the tracking algorithms. These non-conventional approaches are based on maximum likelihood (MEDLL) or non-linear online filtering algorithms (MEPF). Alternative approaches in the positioning algorithms have also been analyzed (VTL and DPE).
To check the performance of these techniques, we have developed a simulation platform, able to carry out deterministic multipath simulations, in which the multipath environment can be controlled deterministically, and stochastic simulations based on tested multipath models (CSCM and DLR). The CSCM model is capable of simulating realistic multipath environments but with the capability to control the multipath ray parameters and the number of these rays. The DLR model allows us to perform simulations based on the conditions in realistic urban environments.
This article has focused on the CSCM model. It shows simulations for a pedestrian and a vehicular scenario that represent typical dynamic conditions for each kind of user.
These results show that the MEDLL technique performs very well in a static multipath environment under low dynamics with good visibility conditions.
Concerning the MEPF, it has been found that the adjustment of the covariance for the observables is very important for achieving good results for the range RMSE. If this adjustment is done well, the results for low values of C/N0 outperform the ordinary DLL/PLL technique. This adjustment has been successfully achieved for a CBOC signal, but a BPSK signal still requires additional work. This may open the possibility of using this technique in applications in which the LOSS is very weak.
It has also been observed that the VTL technique is very effective in high dynamics applications and noisy environments, provided that the internal KF process noise covariance has been properly estimated. VTL also shows a performance very similar to the DLL/PLL+KF scheme in mild-condition scenarios. That makes this technique a good candidate for implementation in a real-time software-based receiver.
Finally, it is necessary to remark that ARTEMISA is still a work in progress. An extensive simulation campaign in a realistic urban environment under different conditions using the DLR multipath model is ongoing. In addition to the techniques presented in this article, other advanced techniques such as direct position estimation are under evaluation.
Acknowledgments
The ARTEMISA project, funded by the European Space Agency (ESA) is being carried out by DEIMOS Space, with the Centre Tecnològic de Telecomunicacions de Catalunya as subcontractor. The content of the present article reflects solely the authors’ views and by no means represents official ESA policy.
The authors of this article would like to thank Tiago Peres, Joao Silva, and Pedro Silva from DEIMOS Engenharia; José Antonio Pulido from DEIMOS Space; and Roberto Prieto-Cerdeira from ESA’s European Space Research and Technology Centre for their support in the adaptation of the GRANADA GNSS Blockset and the simulation platform to the requirements of the techniques and multipath environments tested in the project.
ANTONIO FERNANDEZ co-founded DEIMOS Space with headquarters in Madrid, in 2001, where he is currently in charge of the GNSS Business Unit.
MARIANO WIS is currently working for DEIMOS Space as a project engineer in the GNSS Business Unit. He is also a Ph.D. candidate in the Aerospace Science and Technology Program of Universitat Politècnica de Catalunya in Barcelona.
PAU CLOSAS is a senior research associate and head of the Statistical Interference Department in the Communications Systems Division of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) in Barcelona.
CARLES FERNANDEZ–PRADES is serving as head of the Communications Systems Division at CTTC, where he holds a position as senior researcher.
JOSE A. GARCIA is with the Radio Navigation Systems and Techniques Section at the European Space Agency’s European Space Research and Technology Centre (ESA/ESTEC) in Noordwijk, The Netherlands.
FRANCESCA ZANIER is also with the ESA/ESTEC Radio Navigation Systems and Techniques Section.
MASSIMO CRISCI is the head of the ESA/ESTEC Radio Navigation Systems and Techniques Section.
FURTHER READING
• ARTEMISA
“ARTEMISA: New GNSS Receiver Processing Techniques for Positioning and Multipath Mitigation” by A.J. Fernandez, J.A. Pulido, M. Wis, F. Zanier, R. Prieto-Cerdeira, M. Crisci, P. Closas, and C. Fernández-Prades in Proceedings of Navitec 2012, the 6th ESA Workshop on Satellite Navigation Technologies, and the European Workshop on GNSS Signals and Signal Processing, Noordwijk, The Netherlands, December 5–7, 2012, doi: 10.1109/NAVITEC.2012.6423092.
• GRANADA GNSS Blockset
“Factored Correlator Model: A Solution for Fast, Flexible, and Realistic GNSS Receiver Simulations” by J.S. Silva, P.F. Silva, A. Fernández, J. Diez, and J.F.M. Lorga 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. 2676-2686.
• Signal Propagation Statistical Models
“A Location and Movement Dependent GNSS Multipath Error Model for Pedestrian Applications” by A. Steingass, A. Lehner, and F. Schubert 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. 2284-2296.
“Statistical Modeling of the LMS Channel” by F.P. Fontan, M. Vazquez-Castro, C.E. Cabado, J.P. Garcia, and E. Kubista in IEEE Transactions on Vehicular Technology, Vol. 50, No. 6, November 2001, pp. 1549–1567, doi: 10.1109/25.966585.
• Multipath Estimating Delay Lock Loop
“The Multipath Estimating Delay Lock Loop: Approaching Theoretical Accuracy Limits” by R.D.J. Van Nee, J. Siereveld, P. C. Fenton, and B. R. Townsend in Proceedings of PLANS 1994, the Institute of Electrical and Electronics Engineers Position, Location and Navigation Symposium, Las Vegas, Nevada, April 11–15, 1994, pp. 246–251, doi: 10.1109/PLANS.1994.303320.
• Multipath Estimating Particle Filter
“Nonlinear Bayesian Tracking Loops for Multipath Mitigation” by P. Closas, C. Fernández-Prades, J. Diez, and D. de Castro in International Journal of Navigation and Observation, Vol. 2012, Article ID 359128, 15 pages, 2012, doi:10.1155/2012/359128.
“A VDLL Approach to GNSS Cell Positioning for Indoor Scenarios” by F.D. Nunes, F.M.G. Sousa, and N. Blanco-Delgado 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. 1690–1699.
• Direct Position Estimation
“Maximum Likelihood Estimation of Position in GNSS” by P. Closas, C. Fernández-Prades, and J.A. Fernández-Rubio in IEEE Signal Processing Letters, Vol. 14, No. 15, May 2007, pp. 359-362, doi: 10.1109/LSP.2006.888360.
• Some Previous Innovation Columns on Multipath Mitigation
Averna, developer of test solutions and services for communications and electronics device-makers worldwide, today announced it has received a Broadband Technology Report (BTR) Diamond Technology Review ranking of 4 “Diamonds” for its DOCSIS Channel Emulator (DCE).
Now in its ninth year, the BTR Diamond Technology Reviews is a renowned industry program that was developed to recognize some of the top products and solutions available to the cable industry as determined by a distinguished panel of cable telecommunications engineering experts. Engineering executives from Boyer Broadband, Time Warner Cable, Bright House Networks, Suddenlink Communications, Comcast, Charter and Cox were among the third-party judges for the 2013 Diamonds. The judges had this to say about Averna’s DCE:
“A handy and cost-effective solution for equipment manufacturers to test and validate new DOCSIS products that use bonded channels. MSOs may also have an interest in the product for validating new modem modulation profiles or their own internal product validation efforts.”
“Improved DOCSIS performance testing capabilities, at lower cost and with smaller footprint, for equipment manufacturers and network operators.”
“A creative way to test DOCSIS networks.”
Averna’s DOCSIS Channel Emulator (DCE) is a small-footprint channel-emulation platform that helps device makers ensure that their DOCSIS and EuroDOCSIS products deliver optimum performance in the field. It is capable of acquiring, impairing and generating up to 24 downstream (DS) channels in real time.
The DCE’s flexible FPGA-based design is powered by National Instruments’ vector signal transceiver (VST), which enables Averna to increase the number of supported channels without increasing the DCE’s hardware footprint. The DCE helps users ensure that their equipment meets standards such as the SCTE 40 specifications or MSO-specific configurations. Furthermore, the DCE’s software-based approach and modular architecture protect the client’s investment, allowing them to easily cover both current and future test needs.
“We are proud to achieve such a high score in the Diamond Technology Reviews,” said Jean-Levy Beaudoin, VP Sales, West USA and Latin America, for Averna. “As the DOCSIS industry moves to high channel counts, the DOCSIS Channel Emulator is the first test instrument specifically developed for this new environment, enabling testing that was not economically and technically possible before. Broadband device makers can now increase test coverage and accelerate testing, improving product quality and time-to-market.”
u‑blox has announced the launch of its newest core positioning platform, the u-blox M8. The new chip forms the basis of u-blox’ upcoming line of positioning modules, which are able to acquire and track different satellite systems concurrently to achieve higher accuracy and reliability.
Supporting all deployed as well as upcoming GNSSs, the platform is based on the UBX-M8030 concurrent multi-GNSS receiver IC which is able to track American GPS, European Galileo, Japanese QZSS, Russian GLONASS, and Chinese BeiDou satellites.
Concurrent tracking of GPS (QZSS) and GLONASS or BeiDou, or concurrent tracking of GLONASS and BeiDou satellites increases performance for applications requiring maximum availability and accuracy. The chip is prepared for the European Galileo system through a future firmware upgrade once the constellation is fully available.
The new platform will ultimately support special functions such as Automotive Dead Reckoning and precision timing to support a wide variety of vehicle, industrial and consumer applications.
To further improve acquisition performance, u-blox’ globally available “AssistNow”assisted-GNSS service for accelerated positioning has been extended for u-blox M8 products; the service supports both GPS and GLONASS, and the validity of downloaded assistance data is now able to support offline operation for up to 35 days.
“With the proliferation of multiple new GNSS systems beyond GPS, our u-blox M8 platform is designed to take full advantage of the increasing number of visible satellites to further increase accuracy and availability, particularly in urban and vehicle-based applications,” said Daniel Ammann, executive vice president, head of the Positioning Product Centre, and co-founder of u-blox, “At the same time we realize the ongoing requirement for extremely low-power and cost-sensitive portable applications where operation with a single GNSS system is more than sufficient. That is why we will continue to offer both u-blox M8 and u-blox 7 based products to the market.
The new u-blox M8 chip is at the heart of u-blox’ next generation of positioning modules based on the company’s popular MAX, NEO and LEA module form factors.
u-blox M8 chips feature low power consumption in concurrent reception mode, thanks to an innovative single-die architecture combined with sophisticated software algorithms. The extended supply voltage supply range and 1.8 V/3.0 V I/O compliance supports a wide variety of system architectures. Sophisticated radio architecture and interference suppression using active jamming detection ensure maximum performance even in GNSS hostile environments. UBX-M8030 chips are available in miniature WL-CSP (2.99 x 3.21 x 0.36 mm) and QFN (5.00 x 5.00 x 0.59 mm) packages. The chip is also available in automotive quality grade according to AEC-Q100.
The new platform maintains backwards compatibility with u-blox 7 modules and QFP chip products which remain in the company’s portfolio as the industry’s lowest power standalone satellite positioning receivers. u‑blox’ capability of delivering GNSS technology in both integrated circuit and form-factor consistent modules provides maximum design flexibility and protects customers’ development investments over successive product generations.
First samples of the multi-GNSS receiver chip UBX-M8030 are available for customer evaluation. Soon, module customers can easily migrate to the MAX, NEO, and LEA form factors, u-blox’ popular, industry-standard module form factors.
Spirent now offers A-GNSS record and playback capabilities for mobile device testing.
Spirent Communications today announced the availability of carrier-approved Assisted GNSS Record and Playback capabilities on its Hybrid Location Technology Solution (HLTS). This new A-GNSS Record and Playback capability provides unprecedented realism and repeatability by recording GNSS signals in the field and delivering synchronized assistance data over a radio access interface to test the A-GNSS positioning performance of mobile devices in the lab.
“With user location playing a key role in most smartphone services and applications, A-GNSS positioning performance greatly influences the end-user experience,” said Nigel Wright, vice president of wireless, Spirent Communications. “This new A-GNSS Record and Playback solution enables device manufacturers and network operators, as well as chipset and technology vendors, to accurately test this essential technology using real-world field conditions. This helps ensure high quality LBS and emergency service performance for every mobile subscriber.”
Combining GNSS signals from multiple satellite positioning systems (such as GPS and GLONASS) with assistance data delivered by the network to the device, A-GNSS is regarded as the most universal and precise positioning technology. As such, it is used in mobile devices to support the location information required by commercial services, social media and emergency services such as E911.
Although established A-GNSS simulation tools play an important role in generating repeatable and reliable controlled environments in the lab, they can have limitations when it comes to representing the full range of challenging conditions experienced by mobile users on live networks. Spirent’s A-GNSS Record and Playback addresses these limitations by capturing conditions in the field and playing them back in a reliable and repeatable lab environment. This helps to reduce device time to market and control testing cost by reducing the need for extensive field testing.
Spirent HLTS is recognized by the industry for its unique capabilities that span a wide range of test requirements from early R&D phases to mobile device acceptance. The HLTS now incorporates Spirent’s GSS6400 GNSS Record and Playback System (RPS), together with patent-pending SimHybrid software that generates and delivers the correct assistance data, synchronized with the recorded GNSS signals.
Perspectives from a senior technical specialist and a production engineer at Newmont Boddington Gold Mine.
Newmont Fleet Management Services now continually monitors and plots the performance of JPS Locata alongside traditional GNSS in an effort to fine-tune the installed infrastructure. Learning to sculpt the perfect network continues as we move our JPS LocataLite transmitters to accommodate an ever-changing and expanding pit design.
Large twelve-meter benches and an aggressive mining plan have seen both North Pit and South Pit at NBG rapidly increase in depth, bringing the problems associated with GPS coverage in a deep-pit environment.
As mine sites develop and evolve, for the first time ever, we have the ability to dictate and control which areas we direct our own positioning coverage, and guarantee we can sustain accurate high-precision navigation wherever we need it. This level of control has just never been available before, and is literally impossible with satellite-based positioning signals. With GPS you just get what you get.
We are rapidly re-evaluating what may now be possible. We believe we are only at the very beginnings of where we can go with the LocataNet in the mining environment.
Staying One Step Ahead. Shape changes from week to week keep operations continuously relocating around the mine benches; this can, in some instances, make optimal positioning of the Jps LocataNet challenging. In the early stages of the project, we relied on producing computer-generated radio-coverage heat-map models of the pit to determine optimum positions for the individual LocataLite transmitters on the pit rim, and this is still a valid path if given the time.
However, with more Jps rovers becoming available, we now tend to make highly accurate predictions about network configuration on the fly. We can now install spare rovers as portable units in light vehicles (LVs) used by technicians onsite. This roaming functionality allows use of the Jps web browser in the rover to instantly validate, in the pit, any changes that may be required for the network before drilling and digging equipment is moved into place. Thus we can monitor real-world signal conditions in specific areas and adjust LocataLite positions to optimize positioning availability for machines that will soon arrive.
The typical network monitoring scenario nowadays is to quickly move a JPS-enabled LV onto a bench or area yet to be drilled or excavated, and review the signals from the individual LocataLite transmitters in real time. Technicians then make any necessary placement changes to the network in advance of any mining equipment arriving. Our ability to now ensure maximum possible positioning and navigation coverage at all times was undreamt of even 12 months ago.
Dual Rover. A modified HP Leica Drill JS System utilizing a Dual Jps Locata Rover has been installed into the drilling supervisors’ LV wagon. The drilling superintendent and supervisors had been exploring ways of moving towards a paperless system that could not only check the drill pattern itself, but also the areas being mined around the pattern. They identified a use-case example where having accurate positional information available in a vehicle enables supervisors to quickly review the construction and positioning of the protective windrows around the drill patterns. Access tracks and bench heights could also be checked without needing to call surveyors in to help. This degree of instantaneous clarity removes the guesswork associated with windrow construction, designed to provide a safety barrier between trucking and drilling operations. Incorrectly placed windrows can lead to potential flow restrictions in either operation, so getting it right the first time in active areas is important.
Mark-up by Night. Drilling supervisors can now accurately measure and monitor progress across the drill patterns using Locata technology to display virtual maps of all active drilling areas. Another spin-off benefit has been the introduction of a fixed point mounted to the front vehicle bullbar, to provide emergency mark-up of patterns during the nightshift when surveyors are not available on site. This helps particularly when a localized Wi-Fi outage prevents drills from downloading the blast pattern to commence drilling operations in an area of the mine. Before Jps, use of this high-precision GPS technology in vehicle had been considered ineffective and impractical because of the unreliable GPS coverage in the bottom of both pits. Plans are now under way to install a similar system into the shovel and auxiliary supervisors’ vehicles.
Forward! Recent group discussions found consensus that the best way to move forward with this technology now is continued integration into GPS, rather than stand-alone systems. Miners are generally a cautious lot: we could hedge our bets through a combined GPS+Locata solution package. Full-scale integration of Locata technology into future standard GPS products is perceived as a way companies such as NovAtel can provide the total package. We envisage a unified system available from all positioning receiver manufacturers that combines the benefits of GPS technology with the evident improvements and back-up that Locata has provided for environments where GPS is unable to function.
We have been in the enviable position of gaining a glimpse into the future, when the power of GPS-style positioning is improved to fill the GPS holes. The results we have obtained are, frankly, addictive. Having experienced this revolution first-hand, it would now be extremely painful to even contemplate going back to our previous GPS-only world.
