Septentrio announced on January 7 that it has successfully implemented BeiDou support in the company’s high-precision receiver software, taking advantage of the recent official release of BeiDou’s Interface Control Document (ICD) to including the Chinese satellite navigation signals into its position-velocity-time (PVT) solution.
According to the Belgian GNSS receiver manufacturer, its engineers “are currently processing further data sets to finalize the implementation of full BeiDou support. Although the BeiDou constellation is still being deployed, the data analysis already shows promising results.”
The top panel of Figure 1 compares the height from a stand-alone solution of GPS-only with a GPS+GLONASS solution and a third (in light blue) including BeiDou. “The value added by BeiDou is more than what was expected from a constellation that is still being deployed,” according to Septentrio business development manager Laurent Le Thuaut. “Although the solution is not aided by differential corrections, the position shows an increase in accuracy when sufficient BeiDou satellites are included.”
The bottom panel of Figure 1 shows that, even with the current BeiDou constellation (15 satellites total, of which five are geostationary over China, five in full mid-Earth orbit similar to GPS and GLONASS, and five in inclined geosynchronous orbit over Asia), the total number of satellites used over the European region reached 26 for a short moment.
Figure 2 shows the L1 pseudorange residuals for all constellations individually. This comparison highlights the advantage of the GPS constellation, which builds on two decades of real-time orbit prediction. The BeiDou orbits are “quite accurate for a relatively young constellation, but show typical meter-level jumps when ephemerides are updated,” according to Septentrio.
Septentrio says that the new feature will soon become available on selected company platforms. Users of its multi-constellation receivers will then benefit from improvements in urban availability and signal integrity, thanks to the augmented signal coverage.
An experimental GPS receiver, built by Surrey Satellite Technology Limited (SSTL), has successfully achieved a GPS position fix at 23,300 kilometers altitude – the first position fix above the GPS constellation on a civilian satellite. The SGR-GEO receiver is collecting data that could help SSTL to develop a receiver to navigate spacecraft in geostationary orbit (GEO) or even in deep space.
GPS is routinely used on Low Earth Orbit (LEO) satellites to provide the orbital position and offer a source of time to the satellite. Spacecraft in orbits higher than the 20,000 km of the GPS constellation, however, can only receive a few of the signals that “spill over” from the far side of the Earth, meaning that the signals are much weaker and a position fix cannot always be secured.
With the support of the European Space Agency (ESA) and the ARTES 4 program, SSTL included the SGR-GEO receiver on the GIOVE-A satellite to prove that a receiver could achieve a position fix from a higher orbit. The SGR-GEO is adapted from SSTL’s SGR range of receivers and incorporates a high-gain antenna and a precise oven-controlled clock. It will demonstrate special algorithms to allow reception of weak signals and an orbit estimator intended to allow a near continuous position fix throughout orbit.
“The results from the SGR-GEO receiver are really encouraging,” said Martin Unwin, principal GNSS engineer at SSTL. “We’re getting higher signal strengths than anticipated and also acquiring side lobes from the GPS transmit antennas, which improves the availability of the usable signals for navigation. With the success of the SGR-GEO receiver, GPS, in combination with Galileo and GLONASS, could soon be helping navigate spacecraft much further away from Earth.”
The experimental GPS receiver onboard GIOVE-A has been inactive for six years while the satellite has been used for its primary purpose of transmitting prototype Galileo signals. GIOVE-A’s retirement in June 2012 has allowed the commissioning of the experiment and is now providing valuable data to SSTL and ESA in support of the future use of spaceborne GNSS receivers at GEO altitudes. Engineers at SSTL will continue operations, testing out, tuning and improving the receiver software onboard GIOVE-A to achieve the best possible performance.
Figure 1. Distribution of the GPS+COMPASS tracking network established by the GNSS Research Center at Wuhan University and used as test network in this study.
Data from a tracking network with 12 stations in China, the Pacific region, Europe, and Africa demonstrates the capacity of Compass with a constellation comprising four geostationary Earth-orbit (GEO) satellites and five inclined geosynchronous orbit (IGSO) satellites in operation. The regional system will be completed around the end of 2012 with a constellation of five GEOs, five IGSOs, and four medium-Earth orbit (MEO) satellites. By 2020 it will be extended into a global system.
By Maorong Ge, Hongping Zhang, Xiaolin Jia, Shuli Song, and Jens Wickert
China’s satellite navigation system Compass, also known as BeiDou, has been in deveopment for more than a decade. According to the China National Space Administration, the development is scheduled in three steps: experimental system, regional system, and global system.
The experimental system was established as the BeiDou-1 system, with a constellation comprising three satellites in geostationary orbit (GEO), providing operational positioning and short-message communication. The follow-up BeiDou-2 system is planned to be built first as a regional system with a constellation of five GEO satellites, five in inclined geosynchronous orbit (IGSO), and four in medium-Earth orbit (MEO), and then to be extended to a global system consisting of five GEO, three IGSO, and 27 MEO satellites. The regional system is expected to provide operational service for China and its surroundings by the end of 2012, and the global system to be completed by the end of 2020.
The Compass system will provide two levels of services. The open service is free to civilian users with positioning accuracy of 10 meters, timing accuracy of 20 nanoseconds (ns) and velocity accuracy of 0.2 meters/second (m/s). The authorized service ensures more precise and reliable uses even in complex situations and probably includes short-message communications.
The fulfillment of the regional-system phase is approaching, and the scheduled constellation is nearly completed. Besides the standard services and the precise relative positioning, a detailed investigation on the real-time precise positioning service of the Compass regional system is certainly of great interest.
With data collected in May 2012 at a regional tracking network deployed by Wuhan University, we investigate the performance of precise orbit and clock determination, which is the base of all the precise positioning service, using Compass data only. We furthermore demonstrate the capability of Compass precise positioning service by means of precise point positioning (PPP) in post-processing and simulated real-time mode.
After a short description of the data set, we introduce the EPOS-RT software package, which is used for all the data processing. Then we explain the processing strategies for the various investigations, and finally present the results and discuss them in detail.
Tracking Data
The GNSS research center at Wuhan University is deploying its own global GNSS network for scientific purposes, focusing on the study of Compass, as there are already plenty of data on the GPS and GLONASS systems. At this point there are more than 15 stations in China and its neighboring regions.
Two weeks of tracking data from days 122 to 135 in 2012 is made available for the study by the GNSS Research Center at Wuhan University, with the permission of the Compass authorities. The tracking stations are equipped with UR240 dual-frequency receivers and UA240 antennas, which can receive both GPS and Compass signals, and are developed by the UNICORE company in China. For this study, 12 stations are employed. Among them are seven stations located in China: Chengdu (chdu), Harbin (hrbn), HongKong (hktu), Lhasa (lasa), Shanghai (sha1), Wuhan (cent) and Xi’an (xian); and five more in Singapore (sigp), Australia (peth), the United Arab Emirates (dhab), Europa (leid) and Africa (joha). Figure 1 shows the distribution of the stations, while Table 1 shows the data availability of each station during the selected test period.
Table 1. Data availability of the stations in the test network.
There were 11 satellites in operation: four GEOs (C01, C03, C04, C05), five IGSOs (C06, C07, C08, C09, C10), and two MEOs (C11, C12). During the test time, two maneuvers were detected, on satellite C01 on day 123 and on C06 on day 130. The two MEOs are not included in the processing because they were still in their test phase.
Software Packages
The EPOS-RT software was designed for both post-mission and real-time processing of observations from multi-techniques, such as GNSS and satellite laser ranging (SLR) and possibly very-long-baseline interferometry (VLBI), for various applications in Earth and space sciences. It has been developed at the German Research Centre for Geosciences (GFZ), primarily for real-time applications, and has been running operationally for several years for global PPP service and its augmentation. Recently the post-processing functions have been developed to support precise orbit determinations of GNSS and LEOs for several ongoing projects.
We have adapted the software package for Compass data for this study. As the Compass signal is very similar to those of GPS and Galileo, the adaption is straight-forward thanks to the new structure of the software package. The only difference to GPS and Galileo is that recently there are mainly GEOs and IGSOs in the Compass system, instead of only MEOs. Therefore, most of the satellites can only be tracked by a regional network; thus, the observation geometry for precise orbit determination and for positioning are rather different from current GPS and GLONASS.
Figure 2 shows the structure of the software package. It includes the following basic modules: preprocessing, orbit integration, parameter estimation and data editing, and ambiguity-fixing. We have developed a least-square estimator for post-mission data processing and a square-root information filter estimator for real-time processing.
Figure 2. Structure of the EPOS-RT software.
GPS Data Processing
To assess Compass-derived products, we need their so-called true values. The simplest way is to estimate the values using the GPS data provided by the same receivers.
First of all, PPP is employed to process GPS data using International GNSS Service (IGS) final products. PPP is carried out for the stations over the test period on a daily basis, with receiver clocks, station coordinates, and zenith tropospheric delays (ZTD) as parameters. The repeatability of the daily solutions confirms a position accuracy of better than 1 centimeter (cm), which is good enough for Compass data processing. The station clock corrections and the ZTD are also obtained as by-products.
The daily solutions are combined to get the final station coordinates. These coordinates will be fixed as ground truth in Compass precise orbit and clock determination. Compass and GPS do not usually have the same antenna phase centers, and the antenna is not yet calibrated, thus the corresponding corrections are not yet available. However, this difference could be ignored in this study, as antennas of the same type are used for all the stations.
Orbit and Clock Determination
For Compass, a three-day solution is employed for precise orbit and clock estimation, to improve the solution strength because of the weak geometry of a regional tracking network. The orbits and clocks are estimated fully independent from the GPS observations and their derived results, except the station coordinates, which are used as known values.
The estimated products are validated by checking the orbit differences of the overlapped time span between two adjacent three-day solutions. As shown in Figure 3, orbit of the last day in a three-day solution is compared with that over the middle day of the next three-day solution. The root-mean-square (RMS) deviation of the orbit difference is used as index to qualify the estimated orbit.
Figure 3. Three-day solution and orbit overlap. The last day of a three-day solution is compared with the middle day of the next three-day solution.
In each three-day solution, the observation models and parameters used in the processing are listed in Table 2, which are similar to the operational IGS data processing at GFZ except that the antenna phase center offset (PCO) and phase center variation (PCV) are set to zero for both receivers and satellites because they are not yet available.
Satellite force models are also similar to those we use for GPS and GLONASS in our routine IGS data processing and are listed in Table 2. There is also no information about the attitude control of the Compass satellites. We assume that the nominal attitude is defined the same as GPS satellite of Block IIR.
Table 2. Observation and force models and parameters used in the processing.
Satellite Orbits. Figure 4 shows the statistics of the overlapped orbit comparison for each individual satellite. The averaged RMS in along- and cross-track and radial directions and 3D-RMS as well are plotted. GEOs are on the left side, and IGSOs on the right side; the averaged RMS of the two groups are indicated as (GEO) and (IGSO) respectively. The RMS values are also listed in Table 3.
As expected, GEO satellites have much larger RMS than IGSOs. On average, GEOs have an accuracy measured by 3D-RMS of 288 cm, whereas that of IGSOs is about 21 cm.
As usual, the along-track component of the estimated orbit has poorer quality than the others in precise orbit determination; this is evident from Figure 4 and Table 3. However, the large 3D-RMS of GEOs is dominated by the along-track component, which is several tens of times larger than those of the others, whereas IGSO shows only a very slight degradation in along-track against the cross-track and radial. The major reason is that IGSO has much stronger geometry due to its significant movement with respect to the regional ground-tracking network than GEO.
Figure 4. Averaged daily RMS of all 12 three-day solutions. GEOs are on the left side and IGSOs on the right. Their averages are indicated with (GEO) and (IGSO), respectively.Table 3. RMS of overlapped orbits (unit, centimeters).
If we check the time series of the orbit differences, we notice that the large RMS in along-track direction is actually due to a constant disagreement of the two overlapped orbits. Figure 5 plots the time series of orbit differences for C05 and C06 as examples of GEO and IGSO satellites, respectively. For both satellites, the difference in along-track is almost a constant and it approaches –5 meters for C05.
Note that GEO shows a similar overlapping agreement in cross-track and radial directions as IGSO.
Figure 5. Time series of orbit differences of satellite C05 and C06 on the day 124 2012. A large constant bias is in along-track, especially for GEO C05.
Satellite Clocks. Figure 6 compares the satellite clocks derived from two adjacent three-day solutions, as was done for the satellite orbits. Satellite C10 is selected as reference for eliminating the epoch-wise systematic bias. The averaged RMS is about 0.56 ns (17 cm) and the averaged standard deviation (STD) is 0.23 ns (7 cm). Satellite C01 has a significant larger bias than any of the others, which might be correlated with its orbits.
From the orbit and clock comparison, both orbit and clock can hardly fulfill the requirement of PPP of cm-level accuracy. However, the biases in orbit and clock are usually compensatable to each other in observation modeling. Moreover, the constant along-track biases produce an almost constant bias in observation modeling because of the slightly changed geometry for GEOs. This constant bias will not affect the phase observations due to the estimation of ambiguity parameters. Its effect on ranges can be reduced by down-weighting them properly. Therefore, instead of comparing orbit and clock separately, user range accuracy should be investigated as usual. In this study, the quality of the estimated orbits and clocks is assessed by the repeatability of the station coordinates derived by PPP using those products.
Figure 6. Statistics of the overlap differences of the estimated receiver and satellite clocks. Satellite C10 is selected as the reference clock.
Precise Point Positioning
With these estimates of satellite orbits and clocks, PPP in static and kinematic mode are carried out for a user station that is not involved in the orbit and clock estimation, to demonstrate the accuracy of the Compass PPP service.
In the PPP processing, ionosphere-free phase and range are used with proper weight. Satellite orbits and clocks are fixed to the abovementioned estimates. Receiver clock is estimated epoch-wise, remaining tropospheric delay after an a priori model correction is parameterized with a random-walk process. Carrier-phase ambiguities are estimated but not fixed to integer. Station coordinates are estimated according to the positioning mode: as determined parameters for static mode or as epoch-wise independent parameters for kinematic mode.
Data from days 123 to 135 at station CHDU in Chengdu, which is not involved in the orbit and clock determination, is selected as user station in the PPP processing. The estimated station coordinates and ZTD are compared to those estimated with GPS data, respectively.
Static PPP. In the static test, PPP is performed with session length of 2 hours, 6 hours, 12 hours, and 24 hours. Figure 7 and Table 4 show the statistics of the position differences of the static solutions with various session lengths over days 123 to 125.
The accuracy of the PPP-derived positions with 2 hours data is about 5 cm, 3 cm, and 10 cm in east, north, and vertical, compared to the GPS daily solution. Accuracy improves with session lengths. If data of 6 hours or longer are involved in the processing, position accuracy is about 1 cm in east and north and 4 cm in vertical. From Table 4, the accuracy is improved to a few millimeters in horizontal and 2 cm in vertical with observations of 12 to 24 hours. The larger RMS in vertical might be caused by the different PCO and PCV of the receiver antenna for GPS and Compass, which is not yet available.
Figure 7. Position differences of static PPP solutions with session length of 2 hours, 6 hours, 12 hours, and 24 hours compared to the estimates using daily GPS data for station CHDU.Table 4. RMS of PPP position with different session length.
Kinematic PPP. Kinematic PPP is applied to the CHDU station using the same orbit and clock products as for the static positioning for days 123 to 125 in 2012.
The result of day 125 is presented here as example. The positions are estimated by means of the sequential least-squares adjustment with a very loose constraint of 1 meter to positions at two adjacent epochs. The result estimated with backward smoothing is shown in Figure 8. The differences are related to the daily Compass static solution. The bias and STD of the differences in east, north, and vertical are listed in Table 5. The bias is about 16 mm, 13 mm, and 1 mm, and the STD is 10 mm, 14 mm and 55 mm, in east, north, and vertical, respectively.
Figure 8. Position differences of the kinematic PPP and the daily static solution, and number of satellites observed.Table 5. Statistics of the position differences of the kinematic PPP in post-processing mode and the daily solution. (m)
Compass-Derived ZTD. ZTD is a very important product that can be derived from GNSS observations besides the precise orbits and clocks and positions. It plays a crucial role in meteorological study and weather forecasting.
ZTD at the CHDU station is estimated as a stochastic process with a power density of 5 mm √hour by fixing satellite orbits, clocks, and station coordinates to their precisely estimated values, as is usually done for GPS data.
The same processing procedure is also applied to the GPS data collected at the station, but with IGS final orbits and clocks. The ZTD time series derived independently from Compass and GPS observations over days 123 to 125 in 2012 and their differences are shown on Figure 9.
Figure 9. Comparison of ZTD derived independently from GPS and COMPASS observations. The offset of the two time series is about -14 mm (GPS – COMPASS) and the STD is about 5 mm.
Obviously, the disagreement is mainly caused by Compass, because GPS-derived ZTD is confirmed of a much better quality by observations from other techniques. However, this disagreement could be reduced by applying corrected PCO and PCV corrections of the receiver antennas, and of course it will be significantly improved with more satellites in operation.
Simulated Real-Time PPP Service
Global real-time PPP service promises to be a very precise positioning service system. Hence we tried to investigate the capability of a Compass real-time PPP service by implementing a simulated real-time service system and testing with the available data set.
We used estimates of a three-day solution as a basis to predict the orbits of the next 12 hours. The predicted orbits are compared with the estimated ones from the three-day solution. The statistics of the predicted orbit differences for the first 12 hours on day 125 in 2012 are shown on Figure 10.
From Figure 10, GEOs and IGSOs have very similar STDs of about 30 cm on average. Thus, the significantly large RMS, up to 6 meters for C04 and C05, implies large constant difference in this direction. The large constant shift in the along-track direction is a major problem of the current Compass precise orbit determination. Fortunately, this constant bias does not affect the positioning quality very much, because in a regional system the effects of such bias on observations are very similar.
Figure 10. RMS (left) and STD (right) of the differences between predicted and estimated orbits.
With the predicted orbit hold fixed, satellite clocks are estimated epoch-by-epoch with fixed station coordinates. The estimated clocks are compared with the clocks of the three-day solution, and they agree within 0.5 ns in STD. As the separated comparison of orbits and clocks usually does not tell the truth of the accuracy of the real-time positioning service, simulated real-time positioning using the estimated orbits and clocks is performed to reveal the capability of Compass real-time positioning service.
Figure 11 presents the position differences of the simulated real-time PPP service and the ground truth from the static daily solution. Comparing the real-time PPP result in Figure 11 and the post-processing result in Figure 8, a convergence time of about a half-hour is needed for real-time PPP to get positions of 10-cm accuracy. Afterward, the accuracy stays within ±20 cm and gets better with time. The performance is very similar to that of GPS because at least six satellites were observed and on average seven satellites are involved in the positioning. No predicted orbit for C01 is available due to its maneuver on the day before. Comparing the constellation in the study and that planned for the regional system, there are still one GEO and four MEOs to be deployed in the operational regional system. Therefore, with the full constellation, accuracy of 1 decimeter or even of cm-level is achievable for the real-time precise positioning service using Compass only.
Figure 11. Position differences of the simulated real-time PPP and the static daily PPP. The number of observed satellites is also plotted.
Summary
The three-day precise orbit and clock estimation shows an orbit accuracy, measured by overlap 3D-RMS, of better than 288 cm for GEOs and 21 cm for IGSOs, and the accuracy of satellite clocks of 0.23 ns in STD and 0.56 in RMS. The largest orbit difference occurs in along-track direction which is almost a constant shift, while differences in the others are rather small.
The static PPP shows an accuracy of about 5 cm, 3 cm, and 10 cm in east, north, and vertical with two hours observations. With six hours or longer data, accuracy can reach to 1 cm in horizontal and better than 4 cm in vertical. The post-mission kinematic PPP can provide position accuracy of 2 cm, 2 cm, and 5 cm in east, north, and vertical. The high quality of PPP results suggests that the orbit biases, especially the large constant bias in along-track, can be compensated by the estimated satellite clocks and/or absorbed by ambiguity parameters due to the almost unchanged geometry for GEOs.
The simulated real-time PPP service also confirms that real-time positioning services of accuracy at 1 decimeter-level and even cm–level is achievable with the Compass constellation of only nine satellites. The accuracy will improve with completion of the regional system.
This is a preliminary achievement, accomplished in a short time. We look forward to results from other colleagues for comparison. Further studies will be conducted to validate new strategies for improving accuracy, reliability, and availability. We are also working on the integrated processing of data from Compass and other GNSSs. We expect that more Compass data, especially real-time data, can be made available for future investigation.
UA240 OEM card made by Unicore company and used in Compass reference stations.
Acknowledgments
We thank the GNSS research center at Wuhan University and the Compass authorities for making the data available for this study.
The material in this article was first presented at the ION-GNSS 2012 conference.
Maorong Ge received his Ph.D. in geodesy at Wuhan University, China. He is now a senior scientist and head of the GNSS real-time software group at the German Research Centre for Geosciences (GFZ Potsdam).
Hongping Zhang is an associate professor of the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing at Wuhan University, and holds a Ph.D. in GNSS applications from Shanghai Astronomical Observatory. He designed the processing system of ionospheric modeling and prediction for the Compass system.
Xiaolin Jia is a senior engineer at Xian Research Institute of Surveying and Mapping. He received his Ph.D. from the Surveying and Mapping College of Zhengzhou Information Engineering University.
Shuli Song is an associate research fellow. She obtained her Ph.D. from the Shanghai Astronomical Observatory, Chinese Academy of sciences.
Jens Wickert obtained his doctor’s degree from Karl-Franzens-University Graz in geophysics/meteorology. He is acting head of the GPS/Galileo Earth Observation section at the German Research Center for Geosciences GFZ at Potsdam.
Topcon Positioning Systems announces the light, ultra-compact dual-frequency positioning engine, the B110 GNSS receiver board. The B110 is the first GNSS board with Topcon’s new Vanguard ASIC, supporting 226 universal channels for GPS, GLONASS and Galileo tracking and scalable positioning from sub-meter DGPS to sub-centimeter RTK.
The B110 board’s small size, low power consumption and flexible communication interfaces make it easy to integrate into any precise positioning application, reducing the time-to-market for OEM customers.
Features that facilitate easy integration include:
Compact 40 x 55mm footprint with low power consumption
226 universal channels with GPS + GLONASS L1/L2, Galileo E1 and SBAS “all in view” tracking
High performance RTK engine
Industry-leading position update rate of 100Hz
SD/MMC card interfaces for quick and easy support for data logging – just add a memory card holder
By Thomas Pany, Nico Falk, Bernhard Riedl, Tobias Hartmann, Günter Stangl, and Carsten Stöber
Innovation Insights with Richard Langley
WHAT IS THE IDEAL GNSS RECEIVER? Well, that depends on what you mean by “ideal.” If we take it to mean the simplest, conceptually, yet the most capable and adaptable receiver, then we would just connect an analog-to-digital converter (ADC) to an antenna and pass the converter’s output to a digital signal processor whose software would transform the received signal into the desired result with the utmost speed and precision. There are certain technological limitations that currently preclude fully developing such a device but we are getting very close to the ideal and practical implementations already exist.
Such a GNSS receiver is an example of a software-defined radio — a radio-communications architecture in which as much of the operation of a receiver (or a transmitter) as feasible is performed by software in an embedded system or on a personal computer (PC).
Now, we can’t simply connect an ADC to an antenna since the strength of GNSS signals falls well below the sensitivity threshold of real ADCs and those that can directly digitize microwave radio frequencies are rather power hungry. Therefore, the front end of a real software GNSS receiver includes a low-noise preamplifier, filters, and one or more downconverters to produce an analog intermediate-frequency signal that passes to a high-speed ADC. The digitized signal is provided at the output of the front end in a convenient format, which, for processing signals on a PC, is typically USB 2.0 with its maximum signaling rate of 480 megabits per second. All baseband signal processing is then carried out in the programmable microprocessor.
Software GNSS receivers offer a number of advantages over their hardware cousins. Foremost is their flexibility, which permits easy and rapid changes to accommodate new radio frequency bands, signal modulation types and bandwidths, and baseband algorithms. This flexibility is beneficial not only for multi-GNSS operation but also for prototyping algorithms for conventional hardware designs. Another advantage is their use in embedded systems such as smartphones and wireless personal digital assistants. Software GNSS receivers are also a boon for teaching, where a student can tweak a particular operating parameter and immediately see the effect. And given their remarkable flexibility, software GNSS receivers can be adapted to a variety of special scientific and engineering research applications such as reflectometry and signal analysis.
In this month’s “Innovation,” we look into the development and capabilities of one modern software GNSS receiver in an effort to answer the question “What is the ideal GNSS receiver for precise positioning research?”
“Innovation” is a regular feature that discusses advances in GPS technology andits applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick.
Personal-computer-based software receivers have found broad use as R&D tools for testing new signal processing algorithms, for analyzing received GNSS signals, and for integrating various sensors with GNSS. Software receivers also provide a consistent framework for GNSS signal samples, correlator values, pseudoranges, positions, assistance data, and sensor (inertial) data, and often act as integration platforms for prototype navigation systems. The distinctive feature of PC-based software receivers is their ability to work in post-processing mode in addition to real-time operation and the support of high-performance central processing units (CPUs).
So far, software receivers are typically not used as operational receivers — neither in the mass market, nor in the professional sector, nor as a reference station where a PC would already be available. The last point can be explained by the fact that most software receivers can only process a limited number of frequency bands (sometimes just L1) and are often limited to small bandwidth signals (such as that of the L1 C/A-code signal or the L2 civil signal (L2C)). Improvements of the PC-based software receiver SX-NSR achieved at the end of 2010 and in early 2011 try to overcome these limitations. They include the first real-time implementation of P-code processing on L2, a unique method for processing the ultra-wide Galileo AltBOC signals on E5, and a method to synchronize two NavPort-4 frontends (each supporting four frequency bands of 15 MHz bandwidth) via a hardware link.
The SX-NSR, which has been developed in cooperation with the Universität der Bundeswehr München in Munich, Germany, runs under the Windows operating system (XP or 7) and supports processing of GNSS signals plus sensor data (such as that from an inertial measurement unit, or IMU) in real time and in post-processing mode. It supports all the civil GPS, GLONASS, Galileo, and Compass signals. User-defined signals can be included by providing the pseudorandom noise (PRN) codes and the associated tracking parameters.
The computational real-time performance can be characterized by two rules-of-thumb for acquisition and tracking. Acquisition is based on a flexible coherent and noncoherent integration and may be accelerated by a graphics card based on the Compute Unified Device Architecture (CUDA) — a parallel-computing architecture developed by Nvidia for graphics processing but also useful for accelerating non-graphics applications. Depending on the graphics card type, a few million or many millions of equivalent correlators are available to detect even the weakest signals quickly. Stable tracking is done with multiple correlators. An x86 CPU core supports around 20 channels (for a laptop) to 30 channels (for a PC) at an average CPU load below 50–60 percent. With that CPU load, the software has enough reserve (in terms of the size of the sample buffer) to cope with latencies introduced by the non-real-time Windows operating system. In post-processing, a virtually unlimited number of channels or correlators is available.
The SX-NSR software typically connects to the NavPort-4 front end via a single USB 2.0 connector. One front end supports four RF paths with 15-MHz bandwidth in the L-band. One band is sampled at 40.96 MHz with 12-bit precision. Small batches of samples are transferred with 12 bits at regular intervals to the PC for increased spectral analysis possibilities but the continuous transfer is usually done with just 2 bits. Decimation by a factor of two (yielding a sample rate of 20.48 MHz) and/or bit reduction are options to limit the data transfer bandwidth on the USB bus. The NavPort also includes configurable notch and finite-impulse-response (FIR) filters working with 12-bit precision and 40.96-MHz data rate. The SX-NSR further supports standard output formats (such as Receiver Independent Exchange (RINEX) format or that of the Radio Technical Commission for Maritime Services (RTCM)), has a graphical user interface, and a freely user-accessible application programming interface (API) in the C programming language.
A reference station was established with the SX-NSR for the International GNSS Service (IGS) Multi-GNSS Experiment (M-GEX) starting on February 1, 2012, at the Observatory Graz in Austria (marker name GRAB). The data is routinely processed by the European Reference Frame analysis center at Observatory Lustbuehel, Graz, Austria, with Bernese Software 5.0, and shows results with a quality that is virtually no different than that of commercial hardware receivers.
All-in-view tracking of the four GNSS constellations on all frequencies (see TABLE 1) has been achieved with an off-the-shelf $1,000 PC, two synchronized NavPorts, and the SX-NSR software. In particular, the front end receives Compass B1, B2, and B3 signals and currently the software is tracking two of the geostationary Earth orbit (GEO) satellites, a few of the inclined geosynchronous orbit (IGSO) satellites, and the medium Earth orbit (MEO) satellites at Graz on B1 and B2. There are plans to implement tracking of the B3 signal for the M1 satellite and that of satellite-based augmentation system (SBAS) satellites on L5.
Table 1. Frequency bands supported by the dual NavPort-4 receiver.
Typical received carrier-to-noise-density-ratio (C/N0) values recorded at station GRAB are shown in FIGURE 1. Freely accessible GRAB data in RINEX format can be downloaded from several data archive sites (see Further Reading online).
The SX-NSR software receiver provides a GNSS development and research framework with the API opening it up for user-implemented algorithms. The user may implement only small portions of new code (such as a special acquisition technique) and for the rest of the receiver rely on the well-known behavior of the SX-NSR’s framework. A number of applications are possible using the flexibility of a software receiver; some of them are described in this article.
Figure 1. C/N0 values for five typical satellites, one each for GPS, GLONASS, Galileo, Compass, and the European Geostationary Navigation Overlay Service (EGNOS) SBAS as recorded at Observatory Graz.
The Front End
The front-end frequency plan was adjusted to have a clean spectrum free of internal interference. This is of utmost importance as software receivers are often used to detect and mitigate interference especially for the Galileo E5 and E6 frequency bands due to overlapping radio navigation services.
An inter-front-end link enables synchronization of two NavPort-4 devices. It generates a synchronous reference clock for a proper phase relationship. Moreover, a trigger is used to adjust the digital data stream of both devices. One possible application of the inter-front-end link technology is to easily double the number of available GNSS frequencies. Another possible application is the building of a dual-antenna solution. For this purpose, each NavPort-4 device handles the same GNSS frequencies, but from different antennas. Whereas within each NavPort, a quad analog-to-digital converter (ADC) synchronously samples the four received GNSS signals, the synchronicity between two NavPorts is more complex.
For the inter-front-end link, both devices have to use the same 10-MHz clock reference for a synchronous setup. This is reached by using the reference clock output of the master device as reference clock input of the slave device as depicted in FIGURE 2. It is also possible to connect both NavPort-4 devices to a single external clock reference.
Figure 2.
Each device generates its own 40.96-MHz sample rate from this reference. The phase difference of the 40.96-MHz sample rate is measured in the master and slave with a phase detector. The first input of the detector is the local 40.96-MHz clock. The second input is the 40.96-MHz clock from the other NavPort-4 with a different phase alignment due to ambiguities in its generation and cable delay. The phase detector measures the phase difference between both clocks. The low-pass-filtered output of this measurement is digitized with an ADC. If this measurement is within a phase range of ±7 degrees at 40.96 MHz, which corresponds to ±14 centimeters, the coarse synchronization is finished. If the value is not within this range, the synchronization algorithm repeats.
After starting the data processing for both devices simultaneously with an implemented digital trigger, the phase difference between master and slave clock could be measured continuously for later fine-tuning in the SX-NSR to achieve an accuracy of much below 1 degree at 40.96 MHz, which corresponds to ±2 centimeters.
The synchronization algorithm is verified by connecting two L1-capable NavPorts to the same antenna. The phase and code delay can then be determined from receiver single-differences of GPS L1 C/A-code-derived phase and code measurements. Actually, this delay estimation is part of an attitude solution (see below) and an example is shown in FIGURE 3. The code delay here is around 50 centimeters and includes the RF filter delay difference of around 40 centimeters (which can be calibrated and is stable over power cycles) in addition to the synchronization delay (here around 10 centimeters). The phase delay is naturally determined modulo one cycle and shows warm-up effects of 1.4 centimeters during the first few minutes of operation.
Figure 3. Inter-front-end hardware delay variation on a GPS L1 signal.
GNSS Reference Station
Although the GPS modernization process is ongoing and more and more L2C-capable satellites are in orbit, tracking of the encrypted P-code signal on L2 is still a key element for any receiver to be considered as a reference station or geodetic receiver. Dual-frequency observations need to be available for the full GPS constellation. A possible option to retrieve full wavelength carrier-phase observations and code ranges on L2 is cross-correlation tracking of the encrypted P-code signal. The receiver computes the cross-correlation function between the raw L1 and L2 samples over a long coherent interval as shown in FIGURE 4. The encrypted P-code stream, identical on L1 and L2, is represented by c(tµ).
Figure 4. Cross-correlation block diagram.
A receiver internal complex carrier is generated (see frequency compensation in Figure 4), whose frequency equals the Doppler shift frequency plus the intermediate-frequency difference between L1 and L2. This frequency is generally different for each satellite. The L1 signal is delayed to compute the cross-correlation function for several code-phase taps.
The cross-correlation function is computed using the predicted Doppler difference based on the Doppler frequency estimated from L1 with complex-valued baseband samples. A number of batches are collected and a post-correlation fast Fourier transform is applied. The parameter values shown in TABLE 2 result in a total coherent integration time of 6.4 seconds.
The result is the cross-correlation function as a function of code phase and Doppler. Using interpolation techniques, the position of the peak is determined, which then gives the delay and Doppler shift of the L2 signal with respect to the L1 signal. The complex argument of the peak value gives the L2-L1 carrier-phase differences. Those differences are filtered and are then added to the L1 parameters to give the L2P code estimates.
We use two first-order Kalman filters (one for the code, one for the phase) to smooth the cross-correlation estimates. The code filter is updated with the estimated delay and the Doppler; the phase filter is updated with the estimated Doppler and phase. Cycle slips are detected if the L1-L2 phase changes are too high. Loss-of-lock is detected by comparing the estimated L2 C/N0 value against a threshold. After several Kalman filter tuning steps, the L2P signal is tracked down to low elevation angles. For example, the GPS Block IIF satellite PRN1 was tracked over a whole pass without cycle slips as shown in the code-minus-carrier plot in FIGURE 5.
Figure 5. Code minus carrier-phase measurements for GPS PRN1 at site GRAB on day of year 106, 2012.
One of the key applications of a professional GNSS receiver is its use as a GNSS reference station. Using a software receiver for this purpose would also provide increased monitoring capabilities to detect (un)intentional inference via RF spectral analysis or to detect signal anomalies due to satellite failures or multipath. Furthermore, it is useful for a number of scientific experiments and research topics such as scintillation monitoring or atmospheric occultation studies.
Other GNSS Signals
The inclusion of new GNSS signals in a software receiver is typically straightforward. The PRN codes need to be loaded and the tracking parameters (such as carrier frequency, integration time, error correction scheme, phase relation of signal components data/pilot, correlator positions, and discriminator type) can be selected without source code modification. If a hand-over from a different signal is performed (such as from GPS L1 to GPS L5) and if the first signal has already been synchronized to the transmit time by decoding the time-of-week, then it is possible to directly resolve the code ambiguity of the new signal. If this is not possible, a navigation message decoder has to be implemented to retrieve the time-of-week, which mostly has to be in the source code.
Galileo AltBOC. An important exception to this rule is the Galileo AltBOC signal due to its high bandwidth. A conventional view on the AltBOC signal processing would require a sample rate of at least two times the total signal bandwidth. Depending on how many outer AltBOC side lobes are considered, this results in a sampling rate of 102 megasamples per second or more. This is undesirable for a cost-efficient software receiver solution, regarding the data transfer and the CPU load. The AltBOC processing inside the SX-NSR relies on the fact that both frequency bands E5a and E5b are sampled coherently and this coherency can be exploited to reconstruct the full AltBOC signal. The accuracy of the AltBOC navigation signal is concentrated in the main BOC sidelobes itself. More specifically, the thermal noise and multipath performance are dependent on the Gabor bandwidth, which represents the curvature of the correlation function at the tracking point. Thus a similar Gabor bandwidth can be obtained by sampling the E5a and the E5b band separately. This is the key idea of the resulting AltBOC processing scheme.
The E5a and E5b signal samples are generated synchronously inside the same ADC chip and are transferred via the USB bus to the PC running the SX-NSR. The SX-NSR first acquires and tracks the signal separately on E5a and E5b. As it is quite efficient to run the E5a and E5b tracking on separate threads (and on separate CPU cores), the combination of E5a and E5b correlation values to E5 correlation values is done at the post-correlation level.
There is no feedback from the E5 channel to the E5a/b channels. The channel maintains its own numerically controlled oscillator (NCO). A dedicated transformation is used to account for NCO differences between the E5a/b NCO values and the E5 NCO values. It is basically a sinc-interpolation in the code-phase direction and accounts for Doppler and carrier-phase differences. The transformed correlation values are added and yield an approximation to the AltBOC correlation function.
The E5 correlation values are used to compute the discriminator values to update the E5 tracking loops. The E5 NCO values are used to compute the code pseudoranges and carrier-phase measurements, the Doppler frequency, and the C/N0 values, which are the primary outputs of the E5 receiver. Although the E5 receiver is a somehow a virtual receiver (that is, without correlators), it has the same user interface including most of the configuration parameters, output (for example, multi-correlator), and API.
With AltBOC tracking, the Galileo satellites deliver code and phase measurements on five different carrier frequencies. A code-minus-carrier plot is shown in FIGURE 6. The code accuracy of the AltBOC signal is striking. The E6 signal is severely impacted by the present interference, and phase tracking is only possible for higher elevation angles.
Figure 6. Code minus carrier-phase measurements for Galileo PRN12 at site GRAB on day of year 104, 2012.
Polyfit and Vector Tracking
A software receiver should provide a transparent way to retrieve pseudorange measurements that is well understood and can be well modeled. It should also provide a flexible input to control tracking NCO values. Both points are important if the receiver is part of larger navigation system (such as an integrated GNSS/INS system). Conventional delay-lock loop (DLL) / frequency-lock loop (FLL) / phase-lock loop (PLL) configuration is one option and is well understood by all GNSS researchers and engineers. It has, however, two major drawbacks. The loops introduce time correlations that cannot be easily modeled in a positioning Kalman filter, especially if low bandwidths (carrier aiding) are used. Second, the internal parameters of a DLL are difficult to match to a deeply coupled GPS/INS system.
One way to overcome this is a method called polyfit tracking based on a rather old Jet Propulsion Laboratory patent (U.S. Patent No. 4821294). The idea behind this is to decouple pseudorange determination from the NCO counters. This is accomplished by forming the pseudoranges at the integrate-and-dump rate (such as 50 Hz) and to add the discriminator values to them. The resulting pseudorange is then obtained via a polyfit over the measurement interval.
The time correlation of the measurements is solely determined by the discriminator values, and they compensate for the NCO correlations. A nice example is the application of this method to vector tracking. In vector tracking the NCO values are determined via a line-of-sight projection of the last position, velocity, and time (PVT) estimate and this estimate is usually slightly delayed. Furthermore, the line-of-sight projection is not perfect due to inevitable modeling errors (such as atmospheric delay errors). Thus the NCO does not follow the received signal as well as for DLL/FLL/PLL tracking. This is not a problem as the difference is captured in the discriminator values. FIGURE 7 shows the output of the method for a measurement interval of 0.5 second, one GPS C/A-code signal and for a dynamic user. The PVT update happens with a delay of about 100 milliseconds, changing the Doppler frequency. This resulting phase slope discontinuity is nicely compensated by the phase discriminator. The actual measurements are marked as brown stars in Figure 7. The method can also be applied to slave a channel to a master channel. This is useful for reflectometry, for example, where the master channel locks onto a line-of-sight signal and the slave channel tracks the reflected signal from sea surface.
Figure 7. NCO-based phases (green) plus discriminator values (yellow) and polyfit for carrier-phase, code, and Doppler tracking (dynamic user, GPS C/A-code tracking).
With multiple correlators (for example, nine correlators equally spaced from -0.5 to 0.3 chip for GPS C/A-code tracking), the polyfit method can be extended in a natural way to estimate and mitigate multipath. Using the polyfit carrier estimate, the multi-correlator values are coherently combined over the measurement interval and then a correlation function model is fitted to it. An eventually presented data bit is estimated and wiped off. The correlator fit starts with the assumption that only the line-of-sight signal is present. If the chi-squared value is above a certain threshold, the correlator fit is repeated assuming additionally one multipath signal. Up to two multipath signals can be estimated.
The performance of this method can be tested with an RF signal generator. The scenario includes the line-of-sight signal (GPS C/A-code) and one multipath signal. The initial multipath delay is 0 meters and increases slowly (5.7 millimeters per second). The standard tracking method uses a multipath-mitigating double-delta code discriminator formed from four correlators (-0.2, -0.1, 0.1, 0.2) and an arctan carrier discriminator. Standard tracking is used to control the NCO values. FIGURE 8 shows that multipath is detected for delays larger than 15 meters. The detection performance depends on the carrier-phase difference of the line-of-sight and multipath signal, but for delays larger than 32 meters, multipath is always detected. If multipath is detected, the corrected ranges and C/N0 values are significantly improved.
Figure 8. SX-NSR real-time carrier-phase multipath detection and mitigation performance for a GPS C/A-code signal with a -10 dB multipath signal (standard tracking shown in black, multipath-estimating discriminator output shown in red).
The polyfit method is used routinely in the reference station and has also been tested in a dynamic scenario. A bus drive near the IFEN office in Poing, Germany, with the antenna mounted on the roof has been carried out. Even in this rural area, short-term shading and multipath severely distort single channel (DLL/PLL) tracking causing rather large position errors (red dots in FIGURE 9).
Source: Thomas Pany, Nico Falk, Bernhard Riedl, Tobias Hartmann, Günter Stangl, and Carsten Stöber
With a simple switch in the software, the NCO control can be switched from DLL/PLL to vector tracking (polyfit tracking is always on with the same fit parameters). If the standard point positioning (SPP) solution is used to control the NCO values (yellow dots), the errors are already drastically reduced because the NCOs follow the position and not the reflected signals. Also, erratic NCO jitter preceding loss-of-lock events is now eliminated. A further improvement is achieved if the PVT solution is computed by a Kalman filter (green dots), giving finally the typical high-navigation accuracy of modern GNSS receivers even with partial signal blocking.
Dual-Antenna Heading Determination
The bus drive mentioned above has actually been carried out with two antennas on the roof top with the aim of demonstrating the dual-antenna performance of the software receiver to determine heading. Two synchronized NavPorts have been used, both receiving GPS C/A-code signals (more frequencies would even be more beneficial and possible, but such a test has not yet been carried out). The software is fully prepared to handle data streams from two antennas and one option is to use the same NCO for both antennas. That is, the master antenna data is used to realize vector tracking and the discriminators of the slave channels capture the relative movement of the slave antenna to the master antenna. Again, polyfit tracking provides a natural framework to cope with this data.
Attitude is determined with receiver single-difference observations. It is beneficial to form this difference as early as possible in the receiver processing, that is, immediately after correlation. Thus carrier-phase tracking is based on receiver single-difference correlators, being the product of the complex-conjugate master prompt correlator and the slave prompt correlator (both obviously for the same GNSS signal). The heading is shown in FIGURE 10. As reference, a GPS/INS system was used that calibrated the IMU during the first 300 seconds. One sees that the polyfit plus difference correlator is able to track the carrier phase continuously over 400 seconds in the rural test drive, although there is high multipath and some shading even for the high-elevation-angle satellites. Switching off only one option (vector tracking or the difference correlator) introduces cycle slips and corrupts the heading solution.
Figure 10. Heading and heading error for the dual-antenna test.
Conclusions
Currently, we see two main applications for software receivers. First, they may replace hardware receivers if the increased software receiver performance/flexibility justifies the increased power consumption and size. Several features have been shown in this article, and the possibility to do post-processing and the high-power CPU for customized algorithms are striking arguments for software receivers. On the other hand, software receivers may be customized by inserting user-specific code via the API offering enormous possibilities.
Acknowledgments
The research leading to the AltBOC results and the difference correlator results has received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement numbers 248151 and 247866, respectively. This article is based, in part, on the award-winning paper “Wide-band Signal Processing Features for Reference Station use of a PC-based Software Receiver: Cross-correlation Tracking on GPS L2P, AltBOC and the Inter-frontend Link for up to Eight Frequency Bands” presented at ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, held in Portland, Oregon, September 19–23, 2011.
Manufacturers
The IFEN GmbH NavPort/SX-NSR receiver at station GRAB is fed by a Leica Geosystems AG LEIAR25.R4 antenna with a LEIT radome. The kinematic test used a NovAtel Inc. SPAN GNSS/inertial system.
THOMAS PANY works for IFEN GmbH in Poing, Germany, as a senior research engineer in the GNSS receiver department. He also works as a lecturer (Priv.-Doz.) at the Universität der Bundeswehr München (UniBwM) in Munich, Germany. NICO FALK works for IFEN GmbH in the receiver technology department. BERNHARD RIEDL works for IFEN GmbH as product manager for SX-NSR. TOBIASHARTMANN works for IFEN GmbH in the receiver technology department. GÜNTERSTANGL is an officer of the Austrian Federal Office for Metrology and Surveying and works half time at the Space Research Institute of the Austrian Academy of Sciences. CARSTENSTÖBER is a research associate at UniBwM.
FURTHER READING
• Authors’ Proceedings Paper
“Wide-band Signal Processing Features for Reference Station Use of a PC-based Software Receiver: Cross-correlation Tracking on GPS L2P, AltBOC and the Inter-frontend Link for up to Eight Frequency Bands” by T. Pany, N. Falk, B. Riedl, T. Hartmann, J. Winkel, and G. Stangl in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 19–23, 2011, pp. 753–766.
• Software GNSS Receiver Algorithms and Implementations
Digital Satellite Navigation and Geophysics: A Practical Guide with GNSS Signal Simulator and Receiver Laboratory by I.G. Petrovski and T. Tsujii with foreword by R.B. Langley, published by Cambridge University Press, Cambridge, U.K., 2012.
Navigation Signal Processing for GNSS Software Receivers by T. Pany, published by Artech House, Norwood, Massachusetts, 2010.
A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach by K. Borre, D.M. Akos, N. Bertelsen, P. Rinder, and S.H. Jensen, published by Birkhäuser, Boston, 2007.
Fundamentals of Global Positioning System Receivers: A Software Approach, 2nd Edition, by J. B.-Y. Tsui, published by John Wiley & Sons, Inc., Hoboken, New Jersey, 2005.
• Galileo Signal Tracking
“Performance Evaluation of Single Antenna Interference Suppression Techniques on Galileo Signals using Real-time GNSS Software Receiver” by A.S. Ayaz, R. Bauernfeind, J. Jang, I. Kraemer, D. Dötterbock, B. Ott, T. Pany, and B. Eissfeller in Proceedings of ION GNSS 2010, the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 21–24, 2010, pp. 3330–3338.
“The International GNSS Service in a Changing Landscape of Global Navigation Satellite Systems” by J.M. Dow, R.E. Neilan, and C. Rizos in Journal of Geodesy special issue, “The International GNSS Service (IGS) in a Changing Landscape of Global Navigation Satellite Systems,” Vol. 83, Nos. 3-4, 2009, pp. 191–198, doi: 10.1007/s00190-008-0300-3.
Advanced Navigation, a developer of 3D navigation technologies, has launched its Spatial product series, featuring NVS Technologies AG’s NV08C-MCM high-performance multiple GNSS-constellation receiver.
The Spatial is a ruggedized miniature GNSS/INS & AHRS system that provides accurate position, velocity, acceleration and orientation under demanding conditions. It combines temperature calibrated accelerometers, gyroscopes, magnetometers and a pressure sensor with an advanced GNSS receiver. These are coupled in a sophisticated fusion algorithm to deliver accurate and reliable navigation and orientation, Advanced Navigation said.
The Spatial product line takes advantage of the NV08C-MCM’s multi GNSS constellation support, ensuring high availability of navigation signals, high sensitivity, providing reliability, accuracy and performance.
Advanced Navigation is a privately owned Australian company that specializes in the development of 3D navigation technologies. The company’s engineers come from a background in mission critical robotics built to military specifications.
The National Marine Electronics Association (NMEA) has released a significantly updated version of NMEA 0183, its well-known standard that enables the interfacing of marine electronics, reports the Martime Executive. Version 4.10 will improve boating safety and navigation through updates and expansions of various electronic communications “sentences” pertaining to a number of navigation and communications devices, including Galileo satellite receivers and Automatic Identification Systems (AIS).
NMEA 0183 defines electrical requirements, data transmission protocol and timing, and specific sentence formats for a 4800-baud serial data bus. Version 4.10 affects shipboard, non-shipboard, and land-based equipment as well as networks for maritime and other industry use. The standard has been expanded to include Galileo. Many of the existing GNSS sentences have been extended to accommodate Galileo and future GNSS improvements.
Version 4.10 replaces V 4.00, created in 2008. The new version is backward-compatible to V 2.00.
Navilock, a trademark of Tragant Handels- und Beteiligungs GmbH, announces a new family of GLONASS receiver products including the NL-662U USB-based receiver, equipped with a u-blox GLONASS chipset.
Since the end of 2011 the Russian satellite navigation system GLONASS has been available worldwide. Similar in functionality to the US-NAVSTAR GPS system, GLONASS satellites transmit positioning data over distinct frequencies (Frequency Division Multiple Access, or FDMA, versus GPS which uses Code Division Multiple Access, or CDMA).
The new GLONASS receiver products have internal patch antenna in various configurations to serve different installation requirements. Four housing variants with USB or serial MD6/TTL interfaces are available for installation on vehicles or boats.
The u-blox GLONASS chipset features high accuracy to support precision location-based applications such as navigation, datalogging or tracking.
The products provide -158-dBm signal sensitivity with extremely low power consumption to insure reliable performance and long battery life. The u-blox GLONASS chipset facilitates hot starts in less than 3 seconds.
For more information, visit Navilock’s website and click on “new products.”
u-blox, a positioning and wireless semiconductors, announces the acquisition of UK-based Cognovo Ltd., a company specializing in software defined modem (SDM) chip development technology. The acquisition extends u-blox’ chip design capabilities to create differentiated products for strategic markets that require 4G communications combined with global positioning.
“This is a very exciting acquisition for u-blox as it positions us as an agile and cost-effective supplier of high-speed wireless modem products based on our own chip IP. This allows us to meet market demand for connected systems that require positioning, connectivity and application-specific functionality on a single integrated circuit,” said Thomas Seiler, u-blox CEO. “This new foundation broadens our serviceable market, and will increase our margins in the automotive, consumer, and industrial sectors. Our first 4G product is planned for 2013.”
Cognovo’s Software Defined Modem (SDM) technology and development tools quickly translate complex radio modem designs into fully characterized low-power semiconductor chips, u-blox said. The combination of technologies from Cognovo and the recently acquired 4M Wireless will result in a new wireless modem platform based on IP owned by u-blox.
Cognovo has already demonstrated its SDM baseband chip running high-speed 4G cellular functionality working with the LTE protocol stack from 4M Wireless at Mobile World Congress. With these acquisitions, u‑blox lays the groundwork for establishing a leading position in 4G wireless modems similar to the strategy that u-blox followed to become a market leader in GPS/GNSS modules, u-blox said. The market for 4G modems used for machine-to-machine (M2M) applications is predicted to grow rapidly, surpassing 20 million units by 2016.
“We are very pleased to deploy our SDM technology within u-blox,” said Gordon Aspin, Cognovo CEO. “With over 300 man-years of R&D invested in our SDM technology, this acquisition brings together the industry’s most advanced software modem development platform with some of the best IC design and GNSS engineers in the world. This will be an unbeatable team.”
Key terms of the transaction include:
Acquisition of 100% of the shares of Cognovo Ltd at a price of 16.5 million US.
Acquisition of key intellectual property and software.
Integration of the Cognovo business and 30 employees into u-blox’ organization.
u-blox is launching the u-blox 7, its next-generation core positioning technology platform. Supporting all deployed as well as soon-to-be deployed GNSS, the platform is based on the UBX-G7020 multi-GNSS receiver integrated chip with low power consumption.
With 7 mW power consumption during continuous navigation, u‑blox’ UBX-G7020 is designed for small portable and power-sensitive devices requiring long battery life, high sensitivity, small size, and fast positioning. GPS, GLONASS, Compass, Russian, QZSS, and Galileo satellite positioning systems plus all satellite-based augmentation systems (SBAS) are supported.
“As the satellite systems expand beyond GPS, u-blox 7 is an important step for our customers to design systems that work with all available global navigation standards, particularly GLONASS which is now fully operational. Our multi-GNSS UBX-G7020 integrated circuit does exactly that while achieving two of the most important features that our customers demand: minimum power consumption and small size,” said Andreas Thiel, executive vice president of R&D Hardware and co-founder of u-blox.
The chip has been designed to support the lowest cost stand-alone solution via minimum eBOM; only eight external components are required resulting in a receiver occupying only 30 mm2 on a two-layer PCB. Standard crystal and TCXO are supported. The chip also provides low-power, autonomous log data output of position, velocity, and time. Support for A-GPS and u-blox’ CellLocate hybrid GNSS/cellular positioning technology is embedded to facilitate advanced telematics applications including indoor positioning. Standard and automotive grade are supported.
First samples of the multi-GNSS receiver chip UBX-G7020 are available for customer evaluation. Shortly afterwards, module customers can migrate to the MAX, NEO, and LEA form factors, u-blox’ module series which will all be upgraded to the new u-blox 7 platform.
u-blox 7 maintains software compatibility with u-blox 5 and u-blox 6, and modules provide drop-in compatibility. Both previous generation platforms remain fully supported, the company said. u-blox’ capability of delivering GNSS technology in both integrated circuit and module form provides maximum design flexibility for a wide variety of applications. To evaluate the performance of the u-blox 7 multi-GNSS platform, evaluation kits supporting all u-blox 7 based chips and modules can be ordered.
u-blox is launching the u-blox 7, its next-generation core positioning technology platform. Supporting all deployed as well as soon-to-be deployed GNSS, the platform is based on the UBX-G7020 multi-GNSS receiver integrated chip with low power consumption.
With 7 mW power consumption during continuous navigation, u‑blox’ UBX-G7020 is designed for small portable and power-sensitive devices requiring long battery life, high sensitivity, small size, and fast positioning. GPS, GLONASS, Compass, Russian, QZSS, and Galileo satellite positioning systems plus all satellite-based augmentation systems (SBAS) are supported.
“As the satellite systems expand beyond GPS, u-blox 7 is an important step for our customers to design systems that work with all available global navigation standards, particularly GLONASS which is now fully operational. Our multi-GNSS UBX-G7020 integrated circuit does exactly that while achieving two of the most important features that our customers demand: minimum power consumption and small size,” said Andreas Thiel, executive vice president of R&D Hardware and co-founder of u-blox.
The chip has been designed to support the lowest cost stand-alone solution via minimum eBOM; only eight external components are required resulting in a receiver occupying only 30 mm2 on a two-layer PCB. Standard crystal and TCXO are supported. The chip also provides low-power, autonomous log data output of position, velocity, and time. Support for A-GPS and u-blox’ CellLocate hybrid GNSS/cellular positioning technology is embedded to facilitate advanced telematics applications including indoor positioning. Standard and automotive grade are supported.
First samples of the multi-GNSS receiver chip UBX-G7020 are available for customer evaluation. Shortly afterwards, module customers can migrate to the MAX, NEO, and LEA form factors, u-blox’ module series which will all be upgraded to the new u-blox 7 platform.
u-blox 7 maintains software compatibility with u-blox 5 and u-blox 6, and modules provide drop-in compatibility. Both previous generation platforms remain fully supported, the company said. u-blox’ capability of delivering GNSS technology in both integrated circuit and module form provides maximum design flexibility for a wide variety of applications. To evaluate the performance of the u-blox 7 multi-GNSS platform, evaluation kits supporting all u-blox 7 based chips and modules can be ordered.
By Saeed Daneshmand, Ali Jafarnia-Jahromi, Ali Broumandan, and Gérard Lachapelle
Most anti-spoofing techniques are computationally complicated or limited to a specific spoofing scenario. A new approach uses a two-antenna array to steer a null toward the direction of the spoofing signals, taking advantage of the spatial filtering and the periodicity of the authentic and spoofing signals. It requires neither antenna-array calibration nor a spoofing detection block, and can be employed as an inline anti-spoofing module at the input of conventional GPS receivers.
GNSS signals are highly vulnerable to in-band interference such as jamming and spoofing. Spoofing is an intentional interfering signal that aims to coerce GNSS receivers into generating false position/navigation solutions. A spoofing attack is, potentially, significantly more hazardous than jamming since the target receiver is not aware of this threat. In recent years, implementation of software receiver-based spoofers has become feasible due to rapid advances with software-defined radio (SDR) technology. Therefore, spoofing countermeasures have attracted significant interest in the GNSS community.
Most of the recently proposed anti-spoofing techniques focus on spoofing detection rather than on spoofing mitigation. Furthermore, most of these techniques are either restricted to specific spoofing scenarios or impose high computational complexity on receiver operation.
Due to the logistical limitations, spoofing transmitters often transmit several pseudorandom noise codes (PRNs) from the same antenna, while the authentic PRNs are transmitted from different satellites from different directions. This scenario is shown in Figure 1. In addition, to provide an effective spoofing attack, the individual spoofing PRNs should be as powerful as their authentic peers. Therefore, overall spatial energy of the spoofing signals, which is coming from one direction, is higher than other incident signals. Based on this common feature of the spoofing signals, we propose an effective null-steering approach to set up a countermeasure against spoofing attacks. This method employs a low-complexity processing technique to simultaneously de-spread the different incident signals and extract their spatial energy. Afterwards, a null is steered toward the direction where signals with the highest amount of energy impinge on the double-antenna array. One of the benefits of this method is that it does not require array calibration or the knowledge of the array configuration, which are the main limitations of antenna-array processing techniques.
Processing Method
The block diagram of the proposed method is shown in Figure 2. Without loss of generality, assume that s(t) is the received spoofing signal at the first antenna.
Figure 2. Operational block diagram of proposed technique.
The impinging signal at the second antenna can be modeled by , where θs and μ signify the spatial phase and gain difference between the two channels, respectively. As mentioned before, the spoofer transmits several PRNs from the same direction while the authentic signals are transmitted from different directions. Therefore, θs is the same for all the spoofing signals. However, the incident authentic signals impose different spatial phase differences. In other words, the dominant spatial energy is coming from the spoofing direction. Thus, by multiplying the conjugate of the first channel signals to that of the second channel and then applying a summation over N samples, θs can be estimated as (1)
where r1 and r2 are the complex baseband models of the received signals at the first and the second channels respectively, and Ts is the sampling duration. In (1), θs can be estimated considering the fact that the authentic terms are summed up non-constructively while the spoofing terms are combined constructively, and all other crosscorrelation and noise terms are significantly reduced after filtering. For estimating μ, the signal of each channel is multiplied by its conjugate in the next epoch to prevent noise amplification. It can easily be shown that μ can be estimated as (2)
where T is the pseudorandom code period. Having and a proper gain can be applied to the second channel in order to mitigate the spoofing signals by adding them destructively as (3)
Analyses and Simulation Results
We have carried out simulations for the case of 10 authentic and 10 spoofing GPS signals being transmitted at the same time. The authentic sources were randomly distributed over different azimuth and elevation angles, while all spoofing signals were transmitted from the same direction at azimuth and elevation of 45 degrees. A random code delay and Doppler frequency shift were assigned to each PRN. The average power of the authentic and the spoofing PRNs were –158.5 dBW and –156.5 dBW, respectively.
Figure 3 shows the 3D beam pattern generated by the proposed spoofing mitigation technique. The green lines show the authentic signals coming from different directions, and the red line represents the spoofing signals. As shown, the beam pattern’s null is steered toward the spoofing direction.
Figure 3. Null steering toward the spoofer signals.
In Figure 4, the array gain of the previous simulation has been plotted versus the azimuth and elevation angles. Note that the double-antenna anti-spoofing technique significantly attenuates the spoofer signals. This attenuation is about 11 dB in this case. Hence, after mitigation, the average injected spoofing power is reduced to –167.5 dBW for each PRN. As shown in Figure 4, the double-antenna process has an inherent array gain that can amplify the authentic signals. However, due to the presence of the cone of ambiguity in the two-antenna array beam pattern, the power of some authentic satellites that are located in the attenuation cone might be also reduced.
Figure 4. Array gain with respect to azimuth and elevation.
Monte Carlo simulations were then performed over 1,000 runs for different spoofing power levels. The transmitted direction, the code delay, and the Doppler frequency shift of the spoofing and authentic signals were changed during each run of the simulation. Figure 5 shows the average signal to interference-plus-noise ratio (SINR) of the authentic and the spoofing signals as a function of the average input spoofing power for both the single antenna and the proposed double antenna processes. A typical detection SINR threshold corresponding to PFA=10-3 also has been shown in this figure.
Figure 5. Authentic and spoofed SINR variations as a function of average spoofing power.
In the case of the single antenna receiver, the SINR of the authentic signals decreases as the input spoofing power increases. This is because of the receiver noise-floor increase due to the cross-correlation terms caused by the higher power spoofing signals. However, the average SINR of the spoofing signals increases as the power of the spoofing PRNs increase.
For example, when the average input spoofing power is –150 dBW, the authentic SINR for the single-antenna process is under the detection threshold, while the SINR of the spoofing signal is above this threshold. However, by considering the proposed beamforming method, as the spoofing power increases, the SINR of the authentic signal almost remains constant, while the spoofing SINR is always far below the detection threshold.
Hence, the proposed null-steering method not only attenuates the spoofing signals but also significantly reduces the spoofing cross-correlation terms that increase the receiver noise floor. Early real-data processing verifies the theoretical findings and shows that the proposed method indeed is applicable to real-world spoofing scenarios.
Conclusions
The method proposed herein is implemented before the despreading process; hence, it significantly decreases the computational complexity of the receiver process. Furthermore, the method does not require array calibration, which is the common burden with array-processing techniques.
These features make it suitable for real-time applications and, thus, it can be either employed as a pre-processing unit for conventional GPS receivers or easily integrated into next-generation GPS receivers. Considering the initial experimental results, the required antenna spacing for a proper anti-spoofing scenario is about a half carrier wavelength. Hence, the proposed anti-spoofing method can be integrated into handheld devices.
The proposed technique can also be easily extended to other GNSS signal structures. Further analyses and tests in different real-world scenarios are ongoing to further assess the effectiveness of the method.
Saeed Daneshmand is a Ph.D. student in the Position, Location, and Navigation (PLAN) group in the Department of Geomatics Engineering at the University of Calgary. His research focuses on GNSS interference and multipath mitigation using array processing.
Ali Jafarnia-Jahromi is a Ph.D. student in the PLAN group at the University of Calgary. His research focuses on GNSS spoofing detection and mitigation techniques.
Ali Broumandan received his Ph.D. degree from Department of Geomatics Engineering, University of Calgary, Canada. He is a senior research associate/post-doctoral fellow in the PLAN group at the University.
Gérard Lachapelle holds a Canada Research Chair in wireless location In the Department of Geomatics Engineering at the University of Calgary in Alberta, Canada, and is a member of GPS World’s Editorial Advisory Board.
