The United States Civil GPS Service Interface Committee (CGSIC) has issued a notice about a problem some receivers are having implementing the correct time. The U.S. Coast Guard Navigation Center has received reports of synchronization issues since the implementation of a leap second on Jan. 21. Users experiencing this problem should contact the receiver manufacturer for a firmware or software update.
Below is the text of the CGSIC notice:
All CGSIC: 2015 GPS Future Leap Second Implementation
The GPS 50 bit-per-second navigation message transmitted by each GPS satellite (specifically Page 18, subframe 4) includes the parameters needed to relate GPS time to UTC (Coordinated Universal Time). That relationship is maintained through leap second implementation transitions by IS-GPS-200 compliant user equipment. For leap second transition, user equipment must utilize the notice regarding a scheduled future delta time due to leap seconds (ÄtLSF), together with the week number (WNLSF) and the day number (DN), at the end of which the leap second becomes effective.
On or about Jan. 21, 2015, those GPS navigation messages began to include future leap second data which indicates an increase in the leap second to become effective at the end of June 2015. IS-GPS-200 revision H, dated 24 Sep 2013 paragraph 20.3.3.5.2.4 Coordinated Universal Time (UTC), documents the appropriate algorithm details to ensure correct utilization of the parameters above (including all potential truncated week number transitions and variations in time of processing relative to satellite upload timing near the future leap second effectivity).
The data upload for the June 30 leap second, initiated with SVN48/PRN07 at 18:33:56z on Jan. 21, was correctly executed. However, there are several receivers brands/models that seem to be mishandling this information and applying the leap second now. This is creating a negative one-second offset in faulty receivers. The U.S. Coast Guard Navigation Center has reports of these receivers causing synchronization issues with radios, computer systems, and data logging equipment.
Users experiencing issues with GPS receivers that began on Jan. 21 should contact the receiver manufacturer to determine if the latest firmware or software patch can correct the issue.
V/R Rick Hamilton
CGSIC Executive Secretariat GPS Information
Analysis Team Lead USCG Navigation Center
703-313-5930
A new module produced by a Chinese company combines GPS and BeiDou for civilian positioning, especially for automobiles. The module has been in development for years, and offers improved accuracy and reliability, according to its makers.
“GPS is a single-mode application. But we what are offering with our new module now is a system that can combine Beidou and GPS services, so that the accuracy and reliability can be improved,” Lin Hongzheng, China Electronics Tech. Group Corporation, told CCTV.
The module is expected to improve accuracy to better than one meter, which is now achievable by the current BeiDou system, according to the module’s developers. Ground stations would improve the accuracy even further. “Hopefully, it will be able to position vehicles in different lanes of a road,” said Hu Jinmin, Shenzhen Road Rover Technology.
Pricing has always been a struggle for Beidou hardware, CCTV said. The market price of the new module has come down to less than 30 yuan, or US$5, similar to that of a GPS module.
“This year, from modules to end products, the Beidou system is ready for massive production and ready to compete in the market,” Hongzheng told CCTV.
Carolyn P. McDonald, president and CEO of NavtechGPS, catches up with GPS World at the ION GNSS+ Conference September 9-12, 2014, at the Tampa Convention Center in Tampa, Florida. Franck Boynton, vice president and CTO of NavtechGPS, also shares about simulators with software, the company’s OEM presence and more.
There has been a lot of GNSS-related news in the past month, so I thought I’d do a quick review of the importance (and possibly unimportance), of news you may have heard about.
Just because some GPS satellites weren’t broadcasting CNAV on L2C and L5 doesn’t mean your receiver isn’t using L2C or L5. On the contrary, if your receiver was designed to handle L2C and L5, it’s likely already been using them. The CNAV is just the message being transmitted on the L2C and L5 carrier along with the code. If your receiver tracks L2C and L5, it’s likely already using the carrier (phase) observations. However, even then there are only a limited number of satellites broadcasting L2C and L5 carriers. Specifically, there are 11 satellites broadcasting L2C and four broadcasting L5, meaning that your receiver is roughly tracking one L5 satellite at any one time during the day and several satellites broadcasting L2C.
The C/A code (NAV) message on L1 that your receiver already uses today is good enough. Your receiver doesn’t need the CNAV message on L2C or L5 to utilize the L2C or L5 carrier observations. That’s not to say there’s no benefit to CNAV on L2C and L5, but for RTK or post-processing, the value is largely in the carrier observations. In the future, when L2C and L5 are fully deployed (or near fully deployed), the L5 CNAV does have some distinct advantages, but that’s a few years down the road. To give you an idea of the benefit of L5 when there are enough GPS satellites broadcasting L5 , take a look at the following illustration published by Dr. Richard Langley from the University of New Brunswick comparing the reduction of code multipath on L1 and L5 of two WAAS GEO satellites.
Reduction of code multipath using L1 and L5 on WAAS GEO Richard B. Langley, Hyunho Rho
For the full text of the Langley/Rho article on L5 and WAAS that appeared in the May 2009 issue of GPS World magazine, click here.
Second GPS IIF Satellite of 2014 Launched May 16
On May 16, the second GPS satellite of 2014 was launched successfully from Cape Canaveral in Florida. It was the sixth model IIF GPS satellite, of which 12 are being built, before transitioning to the next-generation model GPS satellite named GPS III. It began transmitting on May 21, 2014, but is not yet set healthy.
Photo credit: Spaceflight Now.
The GPS model IIF satellite broadcasts the legacy GPS signals as well as the new civilian L2C and L5 signals.
Normally, a launched GPS satellite is set healthy (and automatically begin being used by your GPS receiver) within 30 days of launch, sometimes much sooner. However, the IIF GPS satellite launched in February of this year still hasn’t been set healthy, the reason reportedly being an extended navigation test reported here.
A third GPS IIF satellite is scheduled for launch this year on July 31.
During the post-launch interview last Friday, the Air Force stated that the remaining GPS IIF satellites (six) will be launched by the end of 2016. From previous conversations I’ve had with Air Force officials, they’ve stated that there could be an overlap between IIF and III satellite launches. In other words, the first GPS III satellite could be launched before all IIFs have been launched.
“Since March 15, 2014, the Air Force has been conducting functional checkout on a GPS satellite, designated Space Vehicle Number (SVN) 64. SVN-64 broadcasts a data message that clearly indicates SVN-64 is unusable for navigation. Nevertheless, the U.S. government has confirmed that certain GPS receivers are using data from SVN-64, in violation of GPS interface specifications, resulting in outages or corrupted, inaccurate position calculations.”
CGSIC reports that the GPS continues to operate and is fully functional.
In Australia, faulty GPS receivers on roughly 1,000 fleet vehicles caused an apparent GPS “outage” about a month ago.
The U.S. Air Force GPS Operations Center reported that in mid-May tests, “PRN 30 [was] broadcasting almanac datasets that do not reflect constellation changes that occurred since it was last uploaded with navigation message data. [. . . ] The utilization of these almanacs in a manner that regards the time of week, but neglects or mishandles the week number (effectively executing as if the current week number is the week number associated with these almanac parameters), will result in an increasing error in visibility determination and other almanac based estimations (elevation/azimuth, Doppler shift, SV clock offset from GPS time, etc) as the dataset’s actual week offset from the current week increases.”
First Two FOC Galileo Satellites Arrive in French Guiana for Launch Preparation
The first two Fully Operational Capability (FOC) Galileo satellites arrived in the French Guiana in preparation for launch this summer. When launched into orbit, they will join four IOV (In-Orbit Validation) Galileo satellites launched in 2011 and 2012.
The first FOC satellite launch may signal the beginning of Galileo “production” launches of one pair per quarter. Giuliano Gatti, Head of ESA’s Galileo Space Segment Procurement Office, stated that “A steady stream of satellites is foreseen, coming from OHB to ESTEC for acceptance testing and then on to French Guiana. Thanks to the preparatory work done with these pioneer satellites, future Galileos will be processed more rapidly.”
OHB is the prime contractor for a total of 22 FOC Galileo satellites. Those are in addition to the four IOV Galileo satellites.
The two Galileo satellites in the clean room.
Massive GLONASS System Failure
On April 1, the entire GLONASS system was inoperable for about 11 hours. A second, partial failure involving eight GLONASS satellites occurred on April 14 and lasted for about 30 minutes. There were many reports of RTK receivers not operating properly, and some manufacturers instructed their users to “turn off” GLONASS tracking capability on their receivers.
Subsequently, mathematical mistakes were blamed for the failures. The head of the Russian Space Agency, Oleg Ostapenko, stated that the problem would be fully resolved by mid-May and that there is almost no chance of a similar failure happening again.
Some news media are reporting that such an action by Russia would have an effect on GPS.
It would not.
What they’re talking about is discontinuing operations of some or all IGS (International GNSS Service) GPS stations in Russia. Those stations have nothing to do with the operation of GPS. They are simply CORS (Continually Operating Reference Stations). If anything, it will hurt Russian scientists (and scientists from other countries) more than anyone else.
Russian Rocket Launch Failure
Last week, Russia suffered its fifth rocket launch crash in the past four years. Fortunately for the GNSS user community, the rocket was not carrying any GLONASS satellites.
However, it raises serious concerns about the reliability of Russian rockets and launch procedures. Europe’s Galileo satellites are launched using Russian Soyuz rockets at Europe’s space port in French Guiana.
Figure 1. Overall system architecture for MUSTER: Multi-platform signal and trajectory estimation receiver.
More Receiver Nodes Bring Ubiquitous Navigation Closer
Encouraging results from new indoor tests and advances in collaborative phased arrays come from MUSTER: multiple independently operating GPS receivers that exchange their signal and measurement data to enhance GNSS navigation in degraded signal environments, such as urban canyons and indoors.
By Andrey Soloviev and Jeffrey Dickman
Bringing GNSS navigation further indoors by adding new users to a collaborative network can help realize the concept of ubiquitous navigation. Increasing the number of receiver nodes to improve signal-to-noise ratios and positioning accuracy lies at the heart of the MUlti-platform Signal and Trajectory Estimation Receiver (MUSTER). This article focuses on benefits of integrating multi-node receiver data at the level of signal processing, considering two case studies:
Collaborative GNSS signal processing for recovery of attenuated signals, and
Use of multi-node antenna arrays for interference mitigation.
MUSTER organizes individual receiver nodes into a collaborative network to enable:
Integration at the signal processing level, including:
Multi-platform signal tracking for processing of attenuated satellite signals;
Multi-platform phased arrays for interference suppression;
Integration at the measurement level, including:
Joint estimation of the receiver trajectory states (position, velocity and time); and,
Multi-platform integrity monitoring via identification and exclusion of measurement failures.
To exclude a single point of failure, the receiver network is implemented in a decentralized fashion. Each receiver obtains GNSS signals and signal measurements (code phase, Doppler shift and carrier phase) from other receivers via a communication link and uses these data to operate in a MUSTER mode (that is, to implement a multi-platform signal fusion and navigation solution). At the same time, each receiver supplies other receivers in the network with its signal and measurement data. Figure 1 illustrates the overall system architecture.
Open-loop tracking is the key technological enabler for multi-node signal processing. Particularly, MUSTER extends an open-loop tracking concept that has been previously researched for single receivers to networked GNSS receivers. Signals from multiple platforms are combined to construct a joint 3D signal image (signal energy versus code phase and Doppler shift). Signal parameters (code phase, Doppler shift, carrier phase) are then estimated directly from this image and without employing tracking loops.
Open-loop tracking is directly applied to accommodate limitations of military and civilian data links. To support the functionality of the receiver network at the signal processing level (that is, to enable multi-platform signal tracking and multi-platform phased arrays) while satisfying bandwidth limitations of existing data link standards, individual receivers exchange pre-correlated signal functions rather than exchanging raw signal samples.
Before sending its data to others, each receiver processes the incoming satellite signal with a pre-processing engine. This engine accumulates a complex amplitude of the GNSS signal as a function of code phase and Doppler frequency shift. Receivers then broadcast portions of their pre-correlated signal images that are represented as a complex signal amplitude over the code/Doppler correlation space for 1-ms or 20-ms signal accumulation. For broadcasting, portions of signal images are selected around expected energy peaks whose locations are derived from some initial navigation and clock knowledge.
This approach is scalable for the increased number of networked receivers and/or increased sampling rate of the ranging code (such as P(Y)-code vs. CA-code). The link bandwidth is accommodated by tightening the uncertainty in the location of the energy peak. As a result, the choice of the data link becomes a trade-off between the number of collaborative receivers and MUSTER cold-start capabilities (that is, maximum initial uncertainties in the navigation and clock solution).
Multi-Node Signal Accumulation
An earlier paper that we presented at the ION International Technical Meeting, January 2013, describes the approach of multi-platform signal accumulation for those cases where relative multi-node navigation and clock states are partially known. This section reviews that approach and then extends it to cases of completely unknown relative navigation and clock states. The following assumptions were previously used:
Relative position between networked receivers is known only within 100 meters;
Relative receivers’ velocity is known within 2 meters/second;
Relative clock states are calibrated with the accuracy of 100 nanoseconds (ns) or, equivalently, 30 meters.
These assumptions are generally suitable for a pedestrian type of receiver network (such as a group of cellular phone users in a shopping mall area) where individual nodes stay within 100 meters from each other; their relative velocities do not differ by more than 2 meters/second; and, the clocks can be pre-calibrated using communication signals. In this case, zero relative states are used for the multi-node signal accumulation and subsequent tracking. Figure 2 summarizes the corresponding MUSTER tracking architecture.
Figure 2. Multi-platform tracking architecture for approximately known relative navigation states.
Relative navigation states are initialized based on clock calibration results only: zero relative position and velocity are assumed. These initial states are then propagated over time, based on MUSTER/supplemental tracking results (Doppler frequency estimates and higher-order Doppler terms). Code and frequency tracking states are computed by combining biased and unbiased measurements. Biased measurements are obtained by adjusting supplemental signal images for approximately known relative states only. Unbiased measurements are enabled by relative range/Doppler correction algorithms that estimates range and frequency adjustments for each supplemental receiver.
The Kalman filter that supports the optimal combination of biased and unbiased tracking measurements also includes code-carrier smoothing to mitigate noise in measured code phase. For those cases where multi-platform signals are combined coherently, a standard carrier-smoothing approach is used. When non-coherent signal combinations are applied, a so-called pseudo-carrier phase is first derived by integrating Doppler estimates over time and then applied to smooth the code phase.
Multi-platform signal accumulation and tracking can be extended to include cases where the relative navigation parameters are completely unknown. For such cases, MUSTER implements an adjustment search to find the values of code phase and Doppler shift for each supplemental receiver that maximize the overall signal energy.
Adjustment search must be implemented if MUSTER/supplemental relative states are completely unknown, or if their accuracy is insufficient to enable direct accumulation of multi-platform energy, for example, when the relative range accuracy is worse than 150 meters and an energy loss of at least 3 dB is introduced to the signal accumulation process. For each code phase, Doppler and carrier phase (if coherent integration is performed) from the adjustment search space, a supplemental 1-ms function is adjusted accordingly and then added to the MUSTER function. Multiple 3D GPS signal images are constructed, and the image with the maximum accumulated energy is applied to initialize relative navigation parameters: code phase and Doppler shift adjustments values from the adjustment search space that correspond to the energy peak serve as approximate estimates of relative range and Doppler.
The accuracy of these estimates is defined by the resolution of the adjustment search, which would be generally kept quite coarse in order to minimize the search space. For instance, a 300-meter search grid is currently implemented for the code phase, which enables the resolution of relative ranges within 150 meters only. Hence, to mitigate the influence of relative state uncertainties on the tracking quality, a correction algorithm is applied as described in our earlier paper. Figure 3 shows the overall system architecture.
Figure 3. MUSTER signal-tracking approach for cases of unknown relative states.
The architecture keeps all the previously developed system components and adds the adjustment search capability (red block in Figure 3) to incorporate cases of unknown MUSTER/supplemental receivers’ relative navigation states. To minimize the computational load, adjustment search is performed only for the first tracking epoch. Search results are applied to initialize the estimates of MUSTER/supplemental range and Doppler, which are then refined at each subsequent measurement epoch using a combined biased/noisy tracking scheme.
The updated architecture can support cases of completely unknown relative states, as well as those cases where relative states are coarsely known, but this knowledge is insufficient to directly combine multi-platform signals.
The complete adjustment search is possible. However, it is extremely challenging for actual implementations due to both large computational load and a data exchange rate associated with it. To exemplify, NcodexNDoppler versions of the multi-platform 3D function have to be computed for the case where Ncode code phase and NDoppler Doppler shift adjustment search bins are used and outputs from two receivers are combined non-coherently. A complete search (1023 code bins and 11 frequency bins) requires computation of 11,253 3D functions. This number increases to (11,253)2 or 126,630,009 if the third receiver is added.
In addition, receivers must exchange their complete pre-correlated signal functions, which puts a considerable burden on the computational data link. For instance, the exchange of complete 1-ms functions with the 4-bit resolution of samples (required to track the carrier phase) results in the 45 Mbit/s data rate for only a 2-receiver network. Hence, it is anticipated that for practical scenarios, a reduced adjustment search will be utilized for cases where the accuracy of relative states does not support the direct accumulation of multi-platform signals: for example, when the distance between users in the network exceeds 150 meters. In this case, only segments of 1-ms functions around expected energy peaks (estimated based on approximate navigation knowledge) are exchanged.
Phased Arrays
Multi-platform phased arrays have been developed to enable interference and jamming protection for GNSS network users who cannot afford a controlled reception pattern antenna (CRPA) due to size, weight, and power (SWAP), as well as cost constraints. The multi-node phased array approach presented here cannot match the performance of CRPA, with its careful design, antenna calibration, and precise knowledge of relative location of phase centers of individual elements. However, it can still offer a significant interference protection to networked GNSS users.
The multi-platform phased array implements a cascaded space-time adaptive processing (STAP) as illustrated in Figure 4.
Figure 4. Implementation of multi-platform phased array with cascaded space-time adaptive processing.
Cascaded STAP implements temporal filtering at a pre-correlation stage, while spatial filtering (in a form of the digital beam forming or DBF) is carried out at post-correlation. Cascaded STAP is implemented instead of joint STAP formulation to
remove the need to exchange raw signal samples (which is necessary when DBF is applied at pre-correlation); and,
support a novel DBF approach that does not require precise (that is, sub-centimeter to centimeter-level) knowledge of relative position and clock states between network nodes (described later).
Signal samples are still exchanged for the estimation of signal covariance matrices that are required for the computation of temporal and spatial weights. However, the sample exchange rate is reduced significantly as compared to the joint STAP: for example, only 100 samples are currently being exchanged out of the total of 5000 samples over a 1-ms signal accumulation interval.
The DBF uses the Minimum Variance Distortion-less Response (MVDR) formulation for the computation of spatial weight vector. MVDR constrains power minimization by the undisturbed signal reception in the satellite’s direction: (1)
where Φ is the multi-node signal covariance matrix that is computed based on temporal filter outputs; superscript H denotes the transpose and complex conjugate operation; and, η is the steering vector that compensates for phase differences between array elements for the signal coming from the satellite’s direction: (2)
In (2), u is the receiver-to-satellite line-of-sight (LOS) unit vector; rm is the relative position vector between phase centers of the mth node and MUSTER; (,) is the vector dot product; and, λ is the carrier wavelength.
Following computation of DBF weight, multi-node 1-ms GPS signal functions are combined: (4)
where is the complex 1-ms accumulated signal amplitude of the mth node for the (l,p) bin of the code/carrier open-loop tracking search space. The result is further accumulated (for example, over 20 ms) and then applied for the open-loop estimation of signal parameters.
One of the most challenging requirements of the classical MVDR-based DBF is the necessity to estimate relative multi-node position and clock states at a centimeter level of accuracy. To eliminate this requirement and extend potential applications of multi-node phased arrays, the DBF was modified as illustrated in Figure 5.
Figure 5. Modified DBF for a multi-node phased array with unknown relative navigation states.
The modified approach searches through phase adjustments to supplemental receivers and chooses the adjustment combination that maximizes the output carrier-to-noise ratio (C/N0). As a result, no knowledge of the relative navigation states is needed. For each phase combination, , from the adjustment search space, the satellite lookup constraint is computed as:
(5)
Due to the cyclic nature of the phase, the search space is limited to the [0,2π] region. The search grid resolution of π/2 is currently being used.
The obvious drawback of the exhaustive search-based DBF is that the approach is not scalable for the increased number of network users. However, it can still be efficiently applied to a relatively limited network size such as, for example, five collaborative receivers. In addition, the method does not generally support interference suppression with carrier-phase fidelity. However, code and Doppler frequency tracking statuses are still maintained as it is demonstrated in the next section using experimental results.
Experimental Results
We used two types of experimental setups as shown in Figures 6 and 7, respectively.
The first setup (Figure 6) was used to demonstrate multi-platform signal accumulation with unknown relative states and multi-node phased arrays. Raw GPS signals received by three antennas were acquired by a multi-channel radio-frequency (RF) front-end and recorded by the data collection server. The first antenna served as the MUSTER platform, the second and third antennas were used as supplemental platforms. Relative antenna locations were measured as [-0.00; 0.99; 0.05] m (East, North, Up components) for the MUSTER/supplemental receiver 1; and, [0.16; 0.76; 0.27] m for the MUSTER/supplemental receiver 2.
Figure 6. Test setup 1 applied for multi-platform signal accumulation with unknown relative states and multi-platform phased arrays.
A stationary test scenario was considered. Clock biases were artificially induced to emulate a case of asynchronous network. Clock biases were introduced by converting raw GPS signal samples into the frequency domain (applying a fast Fourier transform (FFT) to 1-ms batches of signal samples); implementing a frequency-domain timing shift; and, converting shifted signals back into the time domain (via inverse FFTs). Multi-platform signal processing algorithms were then applied to raw GPS signals with asynchronous multi-platform clocks.
The second setup (Figure 7) was applied for the demonstration of indoor signal tracking. Two receiver nodes (roof and cart) with independent front-ends were used. The roof node remained stationary, while the cart was moved indoors. Each node in the data collection setup includes a pinwheel GPS antenna, an RF front-end, an external clock for the front-end stabilization, and a data collection computer. Figure 7 illustrates corresponding test equipment for the cart node.
Figure 7. Test setup 2 used for indoor signal tracking.
Multi-Platform Signal Tracking with Unknown Relative States. Two platforms were used to demonstrate the case of completely unknown states (antennas 1 and 3 in Figure 6). The third platform was not used due to the extreme computational burden of the complete adjustment search (about 106 grid points for the case of three platforms). A 0.2-ms (60 km) clock bias was added to GPS signal samples recorded by antenna 3. Complete adjustment search was implemented for the code phase. No adjustment search was needed for the Doppler shift. The use of adjustment search provides approximate estimates of relative shifts in multi-platform code phases. These approximate estimates are then refined using a relative range estimation algorithm. Figures 8 and 9 exemplify experimental results for cases of coherent (C/N0 is 31 dB-Hz) and non-coherent (C/N0 is 29 dB-Hz) multi-platform signal accumulation.
Consistent code- and carrier-phase tracking is maintained for the coherent accumulation case.
Carrier-phase and code-phase error sigmas were estimated as 8.2 mm and 28.8 meters, accordingly. The carrier-smoothed code tracking error varies in the range from –4 to –2 meters for the steady-state region. For the non-coherent tracking case, errors in the carrier smoothed code measurements stay at a level of –5 meters. These example test results validate MUSTER tracking capabilities for the case of completely unknown relative navigation states.
Indoor Signal Processing
The indoor test was performed to demonstrate the ability of MUSTER to maintain signal tracking status under extreme signal attenuation conditions. The test was carried out at the Northrop Grumman campus in Woodland Hills, California, with no window view for the entire indoor segment; all the received GPS signals were attenuated by the building structure. Raw GPS signal data was collected from the test setup shown in Figure 6 and then post-processed with multi-platform signal accumulation algorithm with partially known relative navigation states. A combined 20-ms coherent/0.2-s non-coherent signal accumulation scheme was applied. A complete position solution was derived from five highest-elevation satellites.
As the results for the indoor test show in Figure 10, MUSTER supports indoor positioning capabilities for the entire test trajectory. The GPS-only indoor solution reconstructs the right trajectory shape and size. Solution discontinuities are still present. However, the level of positioning errors (20 meters is the maximum estimated error) is lowered significantly as compared to traditional single-node high-sensitivity GPS implementations where errors at a level of hundreds of meters are commonly observed. This accuracy of the multi-node solution can be improved further when it is integrated with other sensors such as MEMS inertial and vision-aided navigation.
Figure 10. Indoor test results.
Multi-Platform Phased Arrays
For the functionality demonstration of multi-platform phased arrays, live GPS signal samples were collected with the test setup shown in Figure 6. Interference sources were then injected in software including continuous wave (CW) and matched spectrum interfering signals. The resultant data were post-processed with the multi-platform phased array approach described above. Relative navigation and clock states were unknown; the DBF formulation was augmented with the phase adjustment search.
Figures 11 and 12 exemplify experimental results.
Figure 11. Example performance of the multi-platform phased array: PRN 31 tracking results; jamming-to-signal Ratio of 50 dB was implemented for all interference sources.Figure 12. PRN 14 tracking results; jamming-to-signal ratio of 55 dB implemented for all interference sources.
Test results presented demonstrate consistent GPS signal tracking for jamming-to-signal (J/S) ratios from 50 to 55 dB. The steady-state error in the carrier-smoothed code is limited to 5 meters.
Acknowledgment
This work was funded, in part, by the Air Force Small Business Innovation Research (SBIR) grant, Phase 1 and Phase 2, topic number AF103-185, program manager Dr. Eric Vinande.
Andrey Soloviev is a principal at Qunav. Previously he served as a Research Faculty at the University of Florida and as a Senior Research Engineer at the Ohio University Avionics Engineering Center. He holds B.S. and M.S. degrees in applied mathematics and physics from Moscow Institute of Physics and Technology and a Ph.D. in electrical engineering from Ohio University.
Jeff Dickman is a research scientist with Northrop Grumman Advanced Concepts and Technologies Division. His area of expertise includes GPS baseband processing, integrated navigation systems, and sensor stabilization. He holds a Ph.D. in electrical engineering from Ohio University. He has developed high-accuracy sensor stabilization technology and is experienced with GPS interferometry for position and velocity aiding as well as high-sensitivity GPS processing techniques for challenging GPS signal conditions.
Telit Wireless Solutions and Parsec Technologies today announced that a combination of the companies’ technologies results in a low profile companion solution for GPS receiver and antenna. For host devices able to accommodate higher volumetric symmetry, assembly of the components can be made to fit a 6 x 16 x 8 millimeter volume. A flat component arrangement can yield an ultra-low-profile volume of 6 x 16 x 2.4 millimeters.
“Receivers combining the Parsec PTA/PT Family and Telit’s Jupiter SE880 modules deliver good user experience in finished LBS (location-based services) critical products without sacrificing design flexibility, ease of implementation or cost,” said Michael A. Neenan, CEO and founder of Parsec Technologies, Inc. “The combination is ‘bullet-proof’ in providing a rewarding design experience making RF work reliably, passing end-product regulatory compliance testing without re-test.”
“Miniaturization is a major enabler of new application areas for positioning and M2M,” said Taneli Tuurnala, Vice President and Head of GNSS Division of Telit Wireless Solutions. “With the Parsec antennas, the complete receiver features the industry’s ‘smallest landed footprint,’ making it suitable for use in wearable electronics, UBI devices or adapters for the mobile computing industry.”
Along with miniaturization, the receiver outperforms top traditional designs, handling a loss of 10 dB or greater in GPS signal reaching, for example, the typical OBD port under a vehicle’s dashboard where many usage-based insurance (UBI) dataloggers are installed. The PTA1.5M active antenna delivers the ultra-sensitive Jupiter SE880 micro receiver over 15 dB of additional gain in the operating frequency range. Both companies are making available complete application notes to simplify the engineering effort for system integrators.
The miniature 4.7 x 4.7 millimeter LGA (Land Grid Array), SiRFstarIV-based Jupiter SE880 receiver module employs heterogeneous 3D integrated technology to achieve best-in-class performance in all dimensions critical for regular or size-constrained GPS applications. Its RF front-end employs spatially calibrated waveguide-quality radio paths inside the three-dimensional space of its architecture, reducing parasitic impedances characteristic of traditional 2-D RF designs. Inside, a multi-filter system includes not only the traditional SAW filters typical in GPS receiver designs but also a 2.4-GHz notch-filter capable of nullifying the jamming effects of high-energy radio devices such as Wi-Fi hot-spots, Bluetooth systems, cordless phones, and others, which greatly affect a GPS receiver’s ability to resolve timid satellite signals in the hostile radio environment where they need to operate.
The PTA1.5M, with a gain of 15dB, and PTA1.5x2M, with a gain of 30dB, are tiny GNSS active antenna modules capable of receiving signals down to -192 dBm with frequency centered at 1575.42 (±1.023) MHz. Either model delivers a radiated efficiency greater than 60% when mated to the Jupiter SE880 receiver. Parsec’s PT1233D LNA also has the highest available IP3 at low voltage, helping eliminate interference. Both PTA1.5M and PTA1.5x2M can incorporate the antenna element, an optional SAW filter, the cascadable PT1233D LNA, matching and passives components, on a low cost, easy to integrate 10×16 mm single sided PCBA with “back side” copper clad ground plane. The height of the PTA1.5M and PTA1.5x2M modules vary according to application, allowing their use in even the smallest form factors including Intel’s M.2 Next Generation Form Factor (NGFF) module (23x30x2.4 mm, LxWxH).
The open architecture design of MAGR 2000-S24 allows modernization and upgrade of GPS functional capabilities through replacement of a single electronics module.
Raytheon Company has received a $15.8 million contract order for its MAGR 2000-S24 miniaturized airborne GPS receiver. The order, which includes new production and sustainment of existing systems, is the first under an Indefinite Delivery-Indefinite Quantity (IDIQ) contract awarded to the company in September 2013 by the U.S. Air Force Space and Missile Systems Center.
Raytheon recently completed its 2,000th delivery of MAGR 2000 systems to the U.S. military and its allies in Europe, the Middle East and the Asia Pacific region. Flown on 20 types of fixed- and rotary-wing platforms, the MAGR 2000-S24 provides unparalleled navigation accuracy and resistance to interference and jamming. Its open architecture design allows for insertion of future GPS modernization enhancements, such as the new military code signal, without having to replace the unit itself.
“With the increasingly sophisticated threats posed by potential enemy nations, our customers recognize Raytheon as the gold standard for highly secure, highly adaptable GPS receivers for the airborne military environment,” said Sharon Black, director of GPS and Navigation Systems for Raytheon’s Space and Airborne Systems business. “Our innovative MAGR 2000-S24 design makes future unit replacement virtually unnecessary. Capability enhancements are as simple as swapping out the electronics module card, providing a highly cost-effective path for keeping fleets up-to-date with the latest GPS technology.”
The current MAGR 2000-S24 IDIQ contract extends through September 2017. The first order of 323 production units is scheduled for completion in June 2015.
Linx Technologies announces its launch of the self-contained, high-performance FM GPS receiver modules. At 15 x 13 millimeters in size, the MediaTek MT3339-based FM Series gives the module fast lock times and high position accuracy even at low signal levels, the company said.
The module’s very low power consumption helps maximize run times in battery powered applications, such as positioning and navigation, location tracking, marine, and asset management, according to Linx Technologies.
Using the built-in MediaTek MT3339 chipset, The FM module can simultaneously acquire on 66 channels and track on up to 22 channels, providing standard NMEA data messages through a UART interface. A simple serial command set can be used to configure optional features.
The GPS receiver is completely self-contained and only requires an antenna. It powers up and outputs position data without any software set-up or configuration. As a result, the FM Series is easy to integrate, the company said.
With built-in hybrid ephemeris prediction technology, the FM Series predicts satellite positions for up to three days and delivers start times of less than 15 seconds under most conditions.
In addition, the available GPS Master Development System connects a FM Series Evaluation Module to a prototyping board with a color display that shows coordinates, speedometer and compass for mobile evaluation. A USB interface allows simple viewing of satellite data and Internet mapping, as well as custom software application development.
Parsec Technologies, Inc., today announced that the company’s Micro-Mini PTA/PT family of GNSS/GPS receiver modules seamlessly integrates with the Telit Wireless Solutions Jupiter SE880 three-dimensional system-in-a-package (3D-SIP) to enable what it calls the world’s smallest- to-date, commercial-class, low-cost GPS L1 receiver.
By combining a PTA/PT family module with the Jupiter SE880 3D-SIP, OEMs and integrators are able to deliver a location-based service product with a GPS L1 receiver in a landed form factor of 20 x 20 x 6 millimenters, or 20 x 10 x 6 millimeters, depending upon component orientation. This contrasts with landed GPS receiver sizes integrating a passive ceramic patch antenna that measure 25 x 25 millimeters, and which Parsec says don’t match the GNSS/GPS frequencies in performance despite being four times the surface area.
With the Parsec/Jupiter combination, integrators can design LBS-critical products with exceptional user experience in applications with severe use, such as obstructed satellite view and high path loss, including indoors, urban canyons, wearables, smart watches, vehicle under-dash on board diagnostics (OBD) devices, metal containers and aircraft fuselage asset tracking, and M.2/next-generation form factor (NGFF) products.
Receivers combining Parsec PTA/PT Family and Telit Jupiter SE880 modules deliver good user experience in finished LBS critical products without sacrificing design flexibility, ease of implementation, or cost, Parsec said. The combination is fully vetted and “bulletproof” in providing a rewarding design experience making RF work reliably, passing end-product regulatory compliance testing without re-test.
According to Michael A. Neenan, CEO and founder of Parsec, customers are future-proofing their LBS critical products when they select a Parsec PTA/PT family for integration with the Telit Jupiter SE880 3D-SIP. PTA/PT family modules receive and amplify any GNSS system signal from 1560 to 1610 MHz, including GPS L1/L1C, with high radiated efficiency in any end product orientation.
Telit Jupiter SE880 reference design kits will soon be equipped with the Parsec PTA/PT family module. OEMs and integrators can order GPS L1 receiver solutions today.
The PTA/PT family of GNSS/GPS receiver Micro-Mini modules extends Parsec’s decade-long tradition of stretching the state-of-the-art in small size, ultra-linearity, miniscule energy usage, high gain, low noise, Any Voltage operation, Field-and-Forget reliability, low cost, and ease of integration/use.
iGage Corporation announces the introduction of a simple, low-cost, L1/L2/L2C GPS receiver specifically designed to use the National Geodetic Survey’s OPUS online post-processing service. The X90-OPUS has a single button interface and customized firmware/software to automate data submissions to OPUS for centimeter-level post-processing anywhere in the United States.
At only US$2,450, iGage reports that the X90-OPUS is the least expensive L1/L2 GPS surveying receiver in the world. Its 4GB internal memory stores more than four years of 15-second interval data. A simple plug-and-play USB connection behaves exactly like a USB memory stick on the user’s computer for easy data download.
The X90-OPUS Download software has single button download. Another button press decimates occupation data, ZIPS the observation file, and automatically fills in the entire OPUS online submission form.
Photo: iGage
According to the announcement, the X90-OPUS makes field surveying easy, no data collector is required just press the power button to begin and end an observation.
“We have taken our years of static GPS surveying experience and boiled it down to a simple one-button operation,” said Mark Silver, President of iGage. “You turn it on and it works. There are no data collectors and no complicated displays. It is drop-dead simple.”
The X90-OPUS receiver is waterproof, submersible, nonincendive, and it floats (IP-67). It carries a standard two-year warranty. The package includes two rechargeable batteries, a dual slot charger, external power cable, and hard shell carrying case.
At 3.1 lbs and less than 8” in diameter, the 24-channel L1/L2/L2C X90-OPUS is ideal for surveying using OPUS, OPUS-RS, OPUS Projects, and standard static surveying campaigns. With a low cost of ownership, the X90-OPUS virtually eliminates the need for “leap-frogging” GPS receivers in large static campaigns.
The X90-OPUS is available immediately. Details are available at http://www.x90gps.com.
China’s icebreaker Xuelong, or Snow Dragon,returned to Shanghai April 9 after successfully completing China’s 29th Antarctica scientific expedition. As a high-accuracy GNSS solutions provider, ComNav supplied a GPS+BeiDou GNSS receiver for this expedition. This was the first time that the ComNav GNSS receiver worked in such an extreme environment.
The reliable performance of the receiver impressed the expedition team. “The fast-searching satellites speed and the accurate positioning result saved us lots of time in the extreme cold field,” said one team member. It was the first time that a BeiDou receiver was used in the Antarctic, according to ComNav.
The research vessel left the southern port city of Guangzhou on November 5, 2012, for Antarctica. It covered 29,000 nautical miles over its 156-day southern voyage, among which 6,000 nautical miles were in ice regions. A total of 239 researchers on board completed 53 research tasks on biology, ecology, geophysics, ocean, climate, environment and glacier, and engineering construction missions.
A Case History Using the New Galileo E6-B/C Signal
By Sergei Yudanov, JAVAD GNSS
A method of decoding an unknown pseudorandom noise code uses a conventional GNSS antenna and receiver with modified firmware. The method was verified using the signals from the Galileo In-Orbit Validation satellites.
Decoding an unknown GNSS pseudorandom noise (PRN) code can be rather easily done using a high-gain steerable dish antenna as was used, for example, in determine the BeiDou-M1 broadcast codes before they were publicly announced. The signal-to-noise ratio within one chip of the code is sufficient to determine its sign. This article describes a method of getting this information using a conventional GNSS antenna and receiver with modified firmware. The method was verified using the signals from the Galileo In-Orbit Validation (IOV) satellites. In spite of the fact that only pilot signal decoding seems to be possible at first glance, it is shown that in practice data signals can also be decoded.
Concept
The idea is to do coherent accumulation of each chip of an unknown signal during a rather long time interval. The interval may be as long as a full satellite pass; for medium Earth orbits, this could be up to six hours. One of the receiver’s channels is configured in the same way as for signal tracking. The I and Q signal components are accumulated during one chip length in the digital signal processor, and these values are added to an array cell, referenced by chip number, by the processor. Only a limited amount of information need be known about the signal: its RF frequency; the expected chip rate; the expected total code length; and the modulation method.
The decoding of binary-phase-shift-keying (BPSK) signals (as most often used) is the subject of this article. It appears that the decoding of more complicated signals is possible too, but this should be proved. A limitation of this method (in common with that of the dish method) is the maximum total code length that can be handled: for lengths greater than one second and bitrates higher than 10,000 kilobits per second, the receiver’s resources may not be sufficient to deal with the signal.
Reconstructing the Signal’s Phase
This method requires coherency. During the full accumulation period, the phase difference between the real signal phase and the phase of the signal generated by the receiver’s channel should be much less than one cycle of the carrier frequency. Depending on the GNSS’s available signals, different approaches may be used. The simplest case is reconstruction of a third signal while two other signals on different frequencies are of known structure and can be tracked.
The main (and possibly the only significant) disturbing factor is the ionosphere. The ionospheric delay (or, more correctly, the variation of ionospheric delay) is calculated using the two known tracked signals, then the phase of the third signal, as affected by the ionosphere, is predicted.
The final formula (the calculations are trivial and are widely available in the literature) is:
where:
φ1 , f1 are the phase and frequency of the first signal in cycles and Hz, respectively
φ2 , f2 are the phase and frequency of the second signal in cycles and Hz, respectively
φ3 , f3 are the phase and frequency of the third signal in cycles and Hz, respectively.
It was confirmed that for all pass periods (elevation angles less than 10 degrees were not tested), the difference between the calculated phase and real phase was always less than one-tenth of a cycle. GPS Block IIF satellites PRN 1 and PRN 25 were used to prove this: the L1 C/A-code and L5 signals were used as the first and second signals, with the L2C signal as the third unknown.
If two known signals are not available, and the ionospheric delay cannot be precisely calculated, it is theoretically possible to obtain an estimate of the delay from one or more neighboring satellites with two signals available. Calculations and estimations should be carried out to investigate the expected precision.
The Experiment
The Galileo E6-B/C signal as currently transmitted by the IOV satellites was selected for the experiment, as its structure has not been published. The E6 signal has three components: E6-A, E6-B and E6-C. The E6-A component is part of the Galileo Public Regulated Service, while the two other components will serve the Galileo Commercial Service. The E6-B component carries a data signal, while the E6-C component is a pilot signal.
From open sources, it is known that the carrier frequency of the E6 signal is 1278.75 MHz and that the E6-B and E6-C components use BPSK modulation at 5,115 chips per millisecond with a primary code length of one millisecond. E6-B’s data rate is 1,000 bits per second and the total length of the pilot code is 100 milliseconds (a secondary code of 100 bits over 100 milliseconds is also present in the E6-C signal, which aids in signal acquisition).
A slightly modified commercial high-precision multi-GNSS receiver, with the E6 band and without the GLONASS L2 band, was used for this experiment. The receiver was connected to a conventional GNSS antenna, placed on a roof and was configured as described above. The E1 signal was used as the first signal and E5a as the second signal. The E6 code tracking (using 5,115 chip values of zero) was 100 percent guided from the E1 code tracking (the changing of the code delay in the ionosphere was ignored). The E6 phase was guided from E1 and E5a using the above equation. Two arrays for 511,500 I and Q samples were organized in firmware. The integration period was set to one chip (200 nanoseconds).
Galileo IOV satellite PRN 11 (also variously known as E11, ProtoFlight Model and GSAT0101) was used initially, and the experiment started when the satellite’s elevation angle was about 60 degrees and lasted for only about 30 minutes. Then the I and Q vectors were downloaded to a PC and analyzed.
Decoding of Pilot Signal (E6-C)
Decoding of the pilot signal is made under the assumption that any possible influence of the data signal is small because the number of ones and zeros of E6-B in each of 511,500 chips of the 100-millisecond integration interval is about the same. First, the secondary code was obtained. Figure 1 shows the correlation of the first 5,115 chips with 5,115 chips shifted by 0 to 511,500 chips. Because the initial phase of the E6 signal is unknown, two hypotheses for computing the amplitude or signal level were checked: [A] = [I] + [Q] and [A] = [I] – [Q], and the combination with the higher correlation value was selected for all further analysis.
Figure 1. Un-normalized autocorrelation of E6-C signal chips.
In Figure 1, the secondary code is highly visible: we see a sequence of 100 positive and negative correlation peaks (11100000001111 …; interpreting the negative peaks as zeros).This code is the exact complement (all bits reversed) of the published E5a pilot secondary code for this satellite. More will be said about the derived codes and their complements later. It appears that, for all of the IOV satellites, the E6-C secondary codes are the same as the E5a secondary codes.
After obtaining the secondary code, it is possible to coherently add all 100 milliseconds of the integration interval with the secondary code sign to increase the energy in each chip by 100 times. Proceeding, we now have 5,115 chips of the pilot signal — the E6-C primary code.
To understand the correctness of the procedure and to check its results, we need to confirm that there is enough signal energy in each chip. To this end, a histogram of the pilot signal chip amplitudes can be plotted (see Figure 2). We see that there is nothing in the middle of the plot. This means that all 5,115 chips are correct, and there is no chance that even one bit is wrong.
Figure 2. Histogram of pilot signal chip amplitude in arbitrary units.
But there is one effect that seems strange at first glance: instead of two peaks we have four (two near each other). We will shortly see that this phenomenon results from the influence of the E6-B data signal and it may be decoded also.
Decoding the Data Signal
The presence of four peaks in the histogram of Figure 2 was not understood initially, so a plot of all 511,500 signal code chips was made (see Figure 3).
Interestingly, each millisecond of the signal has its own distribution, and milliseconds can be found where the distribution is close to that when two signals with the same chip rate are present. In this case, there should be three peaks in the energy (signal strength) spectrum: –2E, 0, and +2E, where E is the energy of one signal (assuming the B and C signals have the same strength).
Figure 3. Plot of 511,500 signal code chip amplitudes in arbitrary units.
One such time interval (starting at millisecond 92 and ending at millisecond 97) is shown in Figure 4. The middle of the plot (milliseconds 93 to 96) shows the described behavior. Figure 5 is a histogram of signal code chip amplitude for the signal from milliseconds 93 to 96.
Figure 4. Plot of signal code chip amplitude in arbitrary units from milliseconds 93 to 96.
Then we collect all such samples (milliseconds) with the same data sign together to increase the signal level. Finally, 5,115 values are obtained. Their distribution is shown in Figure 6.
The central peak is divided into two peaks (because of the presence of the pilot signal), but a gap between the central and side peaks (unlike the case of Figure 5) is achieved. This allows us to get the correct sign of all data signal chips. Subtracting the already known pilot signal chips, we get the 5,115 chips of the data signal — the E6-B primary code. This method works when there are at least some samples (milliseconds) where the number of chips with the same data bit in the data signal is significantly more than half.
Figure 5. Histogram of signal code chip amplitude.Figure 6. Histogram of the signed sum of milliseconds chip amplitude with a noticeable presence of the data signal.
Proving the Codes
The experimentally determined E6-B and E6-C primary codes and the E6-C secondary codes for all four IOVsatellites (PRNs 11, 12, 19, and 20) were put in the receiver firmware. The receiver was then able to autonomously track the E6-B and E6-C signals of the satellites.
Initial decoding of E6-B navigation data has been performed. It appears that the data has the same preamble (the 16-bit synchronization word) as that given for the E6-B signal in the GIOVE Interface Control Document (ICD). Convolutional encoding for forward error correction is applied as described in the Galileo Open Service ICD, and 24-bit cyclic redundancy check error detection (CRC-24) is used. At the time of the analysis, all four IOV satellites transmitted the same constant navigation data message.
Plots of PRN 11 E6 signal tracking are shown in Figure 7 and in Figure 8. The determined codes may be found at env-gpsworld-integration.kinsta.cloud/galileo-E6-codes. Some of these codes may be the exact complement of the official codes since the code-determination technique has a one-half cycle carrier-phase ambiguity resulting in an initial chip value ambiguity. But from the point of view of receiver tracking, this is immaterial.
Figure 7. Signal-to-noise-density ratio of E1 (red), E5a (magenta), E5b (blue), and E6 (green) code tracking of Galileo IOV satellite PRN 11 on December 21–22, 2012.Figure 8. Pseudorange minus carrier phase (in units of meters) of E1 (red), E5a (magenta), E5b (blue), and E6 (green) code tracking of Galileo IOV satellite PRN 11 on December 21–22, 2012.
Acknowledgments
Special thanks to JAVAD GNSS’s DSP system developers. The system is flexible so it allows us to do tricks like setting the integration period to one chip, and powerful enough to be able to do required jobs within a 200-nanosecond cycle. This article was prepared for publication by Richard Langley.
Manufacturers
A JAVAD GNSS TRE-G3T-E OEM receiver, a modification of the TRE-G3T receiver, was used in the experiment, connected to a conventional JAVAD GNSS antenna. Plots of E6 code tracking of all four IOV satellites may be found on the company’s website.
Sergei Yudanov is a senior firmware developer at JAVAD GNSS, Moscow.
