GNSS positioning is highly accurate and reliable — until satellite signals are disrupted. Hexagon | NovAtel has developed SPAN technology that integrates GNSS positioning with inertial measurements for a three-dimensional understanding of position and orientation.
SPAN technology delivers accurate heading, velocity, azimuth, pitch and roll. NovAtel SPAN-enabled receivers and enclosures are effective across applications, including marine environments to monitor heave movements from waves and autonomous vehicles requiring a higher level of precision and integrity.
NovAtel has demonstrated SPAN technology’s capabilities in a sensor-fusion project alongside AImotive and STMicroelectronics. Leveraging sensors on a moving vehicle — GNSS, inertial measurements, and cameras for visual odometry — allowed the teams to produce promising results for continuous positioning on real roads, in underground parking garages, and through tunnels. NovAtel’s PwrPak7-E1 enclosure was used as a reference system in the project, gathering data to confirm the accuracy of the sensor-fusion solution.
Through this project, NovAtel and its partners validated how alternative PNT like SPAN and other sensor fusion solutions complement and extend GNSS positioning availability, accuracy, and reliability.
Swift’s first-of-its-kind, cross-continental drive demonstrates the performance of Skylark. (Image: Swift Navigation)
Swift’s first-of-its-kind, cross-continental drive demonstrates the performance of Skylark.
Swift Navigation, a San Francisco-based tech firm redefining GNSS and precise positioning technology for autonomous vehicles, has completed a cross-country drive test.
The goal of this first-of-its-kind drive, from San Francisco to New York and back, was to measure the efficacy of Swift’s recently expanded Skylark cloud corrections service and to demonstrate true nationwide lane-level GNSS correction coverage at the accuracy, reliability and availability levels required by Swift customers.
The drive took the Swift team across 26 states and Washington, D.C., with 6,614.7 miles (10,645.4 km) driven over 116 hours and 14 minutes logged. A Swift vehicle was equipped with 20 different GNSS devices, tested using six unique chipsets that included: Swift’s Piksi Multi, Duro and multiple leading GNSS silicon providers.
The results of the drive confirmed that Swift’s precise positioning solution — composed of Skylark and the Starling positioning engine — delivers consistent lane-level accuracy at continental level. Skylark delivered 100% availability, with sub-decimeter accuracy, over the entire United States, wherever cellular coverage was available.
Performance highlights from the drive:
+Sub-meter horizontal accuracy (2-sigma) achieved across all environments
100% Skylark availability
Highly repeatable results with Starling + Skylark across variety of dual-frequency GNSS chipsets
“This is the longest continuous GNSS-based precise positioning drive test of its kind and we are proud of the engineering team at Swift for undertaking this ambitious task,” said Anthony Cole, executive vice president of engineering. “The results show that Skylark performs as intended and expected in both open sky and urban environments and demonstrate that Skylark is truly a cross-continental corrections network delivering the high integrity and high availability required by automotive OEMs, last-mile applications, rail, mobile and micro-mobility companies.”
In addition to full contiguous U.S. (CONUS) coverage, the Skylark corrections service is now available in Europe and is being built out to support autonomous applications across the globe.
Download a complete write-up of the cross-country drive test at www.swiftnav.com.
Taoglas, a provider of next-generation internet of things (IoT) solutions, has launched Edge Locate, a GNSS L1/L2/E5 module that combines antenna, RF electronics and receiver technology to deliver reliable centimeter-level positioning.
Taoglas, in partnership with u-blox, created a smart antenna that uses multi-band GNSS technology, providing between 1- to 3-centimeter-level accuracy.
With Edge Locate, manufacturers can quickly and effectively build devices with centimeter-level positioning technology, without having to invest in costly and lengthy RF design, integration and testing processes.
The device features multiband GNSS positioning that can be used in conjunction with cost-effective real-time kinematic (RTK) positioning capability.
Traditionally, most IoT devices use single-band GPS technology, delivering on average 10-meter accuracy with existing GPS modules and antennas, Taoglas said in a press release. This enables location-specific, mission-critical services such as emergency response, smart infrastructure, precision agriculture and microbility mobility applications where precise location provides critical value to the IoT application.
Taoglas can also consult and install the RTK network in any global location for any IoT use case.
“Centimeter-level positioning is absolutely key to the next-generation of IoT enabled applications,” said Ronan Quinlan, Co-CEO of Taoglas. “Take an example from the burgeoning micro-mobility industry. When granting licenses from a trial, the city authorities would like to monitor the riders of e-scooters, ensuring riders are staying off footpaths, or parking in designated areas. The problem is that today’s legacy GPS solutions don’t often know which side of the road a scooter is on. Whereas with our solution, fleet operators can pinpoint within just a few centimeters where a device is located. We do this by working with our customers to enable the whole solution and we make sure it works reliably in real life.”
Edge Locate can greatly accelerate the latest GNSS multiband product launch plans by offering a plug-and-play product that uses a common connector for integration into any electronics device. It also connects directly to the Taoglas Edge board for immediately connectivity options.
Taoglas is exhibiting at Mobile World Congress Americas, Booth 2602 in the South Hall of the Los Angeles Convention Center.
The Swift/Arm partnership means Arm will offer Swift Navigation’s high-integrity, high-accuracy GNSS positioning solutions as an option on Arm-based platforms to developers of autonomous and connected vehicles. (Image: Swift Navigation)
Swift Navigation is partnering with Arm, a global leader in semiconductor IP.
The partnership means Arm will offer Swift Navigation’s high-integrity, high-accuracy GNSS positioning solutions as an option on Arm-based platforms to developers of autonomous and connected vehicles.
Swift Navigation is a San Francisco-based tech firm redefining GNSS positioning technology for autonomous vehicles.
Standard GNSS positioning is three to five meters in depth which is not suitable for safety-critical systems requiring lane-level accuracy. For higher levels of autonomous capability, a vehicle needs to be able to determine its absolute location. To achieve this, high-precision localization is needed to get to accuracy down to the centimeter.
Swift’s partnership with Arm will deliver a high-integrity, high-accuracy GNSS positioning solution for silicon makers and Tier 1 and 2 auto suppliers to integrate precise positioning into the sensor suite.
Swift Navigation’s Starling is a GNSS positioning engine designed for just such automotive and autonomous vehicle applications. Starling’s software enhances the measurements for commercially available GNSS receivers to provide true precision and integrity capabilities. Starling is receiver-agnostic, so it is ideal for Arm customers as it works with a variety of automotive grade chipsets and inertial sensors.
Swift’s partnership with Arm will deliver a high-integrity, high-accuracy GNSS positioning solution for silicon makers and Tier 1 and 2 auto suppliers to integrate precise positioning into the sensor suite. (Image: Swift Navigation)
Swift and Arm are working together to provide developers of autonomous and connected vehicles a cost-effective, scalable and high-integrity positioning solution. Starling is designed to be compatible with industry leading silicon makers who build their solutions on Arm.
Starling works with a variety of GNSS measurements engines and is a hardware proven, end-to-end solution, tunable for the specific requirements of a customer’s platform. This partnership elevates the capabilities of the connected car and simplifies the integration of high-precision GNSS into Tier 1 and 2, Silicon and Platform and Automotive OEM vendors.
“We are pleased to join the ecosystem of Arm technology partners to deliver precise positioning solutions to its automotive and autonomous vehicle customers,” said Timothy Harris, chief executive officer of Swift Navigation. “This partnership opens up a broader audience of customers who can benefit from Swift’s positioning technology and builds on our mission to enable a future of autonomous vehicles.”
“As we strive toward an autonomous future, the requirements of the automotive market are changing, and a more solution-based approach is needed,” said Dipti Vachani, senior vice president and general Manager, automotive and IoT line of business, Arm. “The combination of Arm IP uniquely designed for automotive and Swift’s GNSS solution gives our partners another key component on the road to the effective deployment of autonomous vehicles at scale.”
Available for purchase today for Arm-based processors, the Starling positioning engine provides a rapid deployment, low total cost of ownership solution to enable widespread adoption of ADAS, connected car, C-V2X and autonomous solutions.
Interested parties should visit this website to get more information on using the Starling positioning engine on Arm-based devices.
The joint solution will be also be showcased at the IAA New Mobility World 2019 event from Sept. 10-15 at the Arm booth, Hall 5.0, stand A10, Frankfurt Messegelände.
GMV has been awarded a contract for development of a precise GNSS positioning system with integrity for the new generation of autonomous vehicles of the German carmaker BMW Group.
The Spanish multi-national’s technology solution is going to be developed for the first time in BMW Group’s autonomous vehicles. GMV’s positioning software calculates the vehicle’s position and other magnitudes, using advanced GMV-developed algorithms, including components that have already been patented. These algorithms have been especially modified and adapted to meet BMW Group’s performance and safety requirements.
Photo: BMW Group
The developed software will abide by the most demanding automotive standards and the highest quality levels of safety-critical software, GMV said.
Another key component provided by GMV is a GNSS correction service to be run in a secure infrastructure using data from a global network of monitoring stations to be set up by GMV under this contract.
This new project cements GMV’s position as a supplier of GNSS-based autonomous-car positioning solutions, the company said.
“GMV has been investing for many years in the key GNSS technologies that are essential for autonomous driving systems,” said Miguel Ángel Martínez Olagüe, GMV’s general manager of Intelligent Transportation Systems. “For our company this contract represents a unique opportunity to capitalize on all that effort, providing a product of outstanding performance for the automotive industry.”
SINGLE-FREQUENCY GPS POSITIONING. Can it get any better? In the March 2018 edition of this column, we looked at the development of precise point positioning or PPP — the (mostly) carrier-phase-based positioning technique using satellite orbit and clock data significantly more precise than that available in the broadcast navigation messages. We noted that dual-frequency PPP can achieve horizontal positioning accuracies better than 10 centimeters. On the other hand, single-frequency pseudorange-based GPS positioning using broadcast data (by far, the most common use of GPS) provides meter-level accuracy at best. And “at best” means under ideal conditions with no sky obstructions, negligible multipath, a benign ionosphere and healthy signals.
But what about the more typical conditions experienced while navigating in urban environments such as blocked signals and reception of reflected or non-line-of-sight signals and multipath-contaminated signals? And what if the ionosphere is disturbed to boot? A standard unaugmented single-frequency GPS receiver will be lucky to get consistent accuracies much below 10 meters. In some cases, positioning accuracy is compromised by the relatively inexpensive antenna and receiver hardware used in devices for the mass consumer market. That includes the positioning units in smartphones and vehicle satnav units. True, 10-meter accuracy positioning might be quite acceptable for certain applications including basic navigation to get from point A to point B. But there are many situations that we encounter in our daily lives where a predictable accuracy of 1 meter or better could be hugely useful such as identifying the correct lane in which a vehicle is traveling or identifying a particular parking space — not to mention various vehicle-to-vehicle positioning and situational awareness needs.
Sure, we can augment a GPS receiver with other devices such as inertial sensors, barometers, wheel-speed sensors and the like. And they can, indeed, be a big help. But can we improve the capability of the standalone GPS receiver?
For a long time, the use of multiple-constellation receivers has been touted as a panacea for blocked signals in cities. Since the 1990s, we have had two working satellite constellations: GPS and GLONASS. Yes, GLONASS has had its up and downs, but it has provided a more or less full constellation for a number of years now, and many consumer-level devices include a GLONASS capability nowadays. Some of the latest devices also sport the ability to use signals from the European Galileo and Chinese BeiDou systems now nearing completion.
While one might still have large dilutions of precision using a multi-constellation GNSS receiver, in general, even one additional satellite signal can be beneficial in improving accuracy or navigation continuity. Receiver chips with the ability to provide useful carrier-phase measurements will also be hugely beneficial, and we are already seeing developments in this regard in the smartphone market.
We should also mention that there can be significant differences in the performance of different kinds of antennas and their effect on positioning capabilities in the same environment. And, of course, how the measurements from different satellites are combined in a receiver’s processor can have an effect on the resulting position accuracy.
In this month’s column, I am joined by one of my graduate students, Ivan Smolyakov, who has carried out some real-world tests with the aim of improving single-frequency GNSS positioning in urban environments. The initial tests (using a survey-grade receiver to be replaced with more modest equipment in subsequent testing) concentrated on the benefit of using GLONASS alongside GPS, the effect of different antennas, and adaptive weighting of observations. Single-frequency accuracies below one meter? You bet.
A new generation of mobile platforms equipped with chips allows continuous carrier-phase tracking, lifting applications based on localization to the next level. Whether in transportation, pedestrian navigation or safety-of-life services, a robust position determination is required in various environments including cities.
Navigation in urban environments is significantly challenged by signal degradation. Typical urban scenarios result in blocked signals, reception of non-line-of-sight (NLOS) signals and multipath-contaminated signals. Low-cost single-frequency equipment suffers the most from such effects as a consequence of hardware limitations, while also being affected by potentially poor satellite geometry.
This article addresses the challenge for mobile platforms equipped with low-cost single-frequency receivers and patch antennas to efficiently utilize all GNSS signals available.
Various techniques attempt to minimize the impact of NLOS and multipath on a final solution: weighting based on the elevation angle of a satellite and signal-to-noise ratio of its signal, as well as exclusion of certain satellites from processing, selecting the most consistent set of satellites. In our work, we explored this approach, combining the aforementioned methods with automatic stochastic model adjustment. Signal degradation demonstration and algorithm testing was performed on 1-Hz combined GPS and GLONASS static and kinematic datasets collected in an urban environment. Our proposed algorithm yielded sub-meter-level positioning accuracy and showed a 10 percent accuracy improvement compared to regular weighting and satellite-exclusion-based algorithms.
In the past several years, the number of applications that at least to some extent depend on GNSS has increased dramatically. Precise point positioning (PPP) solutions propagated to common everyday uses and started to lead the way as a key method for coordinate determination in the low-cost regime of navigation. This area could be characterized by the necessity of real-time coordinate determination with a sub-meter/decimeter accuracy requirement and often with the expectation of reaching that level of accuracy in the most challenging environment for satellite navigation: the urban setting.
Tall buildings, tree foliage and the presence of reflective surfaces decrease the number of available satellites and result in reception of NLOS signals, as well as in reception of signals contaminated by multipath. The field of aided navigation addresses the problem by using additional devices and external information along with GNSS, such as tightly coupled inertial sensors or 3D mapping of the surrounding environment. Another way to deal with these degrading effects is to address their existence directly by means of consistency checking and outlier mitigation. However, while being effective, these types of algorithms can often create an excessive computational load, which limits their use for low-cost applications.
On the GNSS side, the problem also could be addressed by detecting faulty signals and adapting filtering parameters accordingly, making sure that incorrect a priori statistical information is not used as it can lead to solution degradation. Many adaptive techniques were developed, reducing the need to accurately know a priori filtering parameters.
Our research attempts to maximize the use of pure GNSS in the context of standalone low-cost single-frequency positioning, adjusting filter parameters in a way consistent with the surrounding environment. First, the vulnerability of low-cost patch antennas towards NLOS and multipath-contaminated signals has been investigated through a comparison to higher quality antennas in an observation campaign carried out in an urban environment. Second, based on preliminary analysis of findings and inspired by past work, we developed an adaptive weight adjustment algorithm with minimal computational load, aiming to address a rapidly changing surrounding multipath environment. The proposed algorithm was tested in GPS-only and combined GPS + GLONASS static and kinematic scenarios.
OBSERVATION CAMPAIGN
The idea behind the observation campaign was to highlight unwanted low-cost patch antenna vulnerability to multipath and NLOS signals. Three antennas were mounted on the roof of a car (see FIGURE 1): a high-grade antenna (Leica AX1203+ GNSS with 29 dB low-noise-amplifier (LNA) gain), a consumer-level patch antenna priced around $150 (Tallysman TW3470 with 40 dB LNA gain) and a truly low-cost patch antenna (Chang Hong Information Co., GPS Active 28 dB Magnetic Antenna) priced around $10.
FIGURE 1. Experimental setup. Tested antennas from left to right: Tallysman TW3470, Leica AX1203+ GNSS, low-cost patch antenna (Chang Hong Information Co.).
Paired with each antenna, we used geodetic quality receivers of the same model (Javad Triumph-LS) with identical configurations, which yielded the best possible performance on the receiver side, meaning that differences in analyzed behavior are mostly dependent on the antenna type. After the start of observations, the experimental setup remained stationary for 30 minutes in a parking lot environment, followed by an approximately 30-minute drive through downtown Fredericton, New Brunswick.
Road situations encountered included passing under a bridge and a traffic jam caused by road construction. These circumstances introduced complete signal blockage, as well as multipath-contaminated and NLOS signal reception. The Javad receivers recorded observables at a 5-Hz rate. We subsequently decimated the data to 1 Hz for post-processing. The GPS and GLONASS L1 pseudorange and carrier-phase observations (C1C and L1C in RINEX terminology) were used for the single-frequency positioning solutions.
METHODOLOGY
The results shown in this article were obtained using post-processing. However, the described technique is ready for implementation in real time. The undifferenced measurements model was selected as an approach commonly adopted for truly low-cost positioning platforms. Multipath is notoriously difficult to reliably estimate in a filter. Instead, our proposed technique takes advantage of the pseudo-multipath (also referred to as “code-minus-phase”) observable and a statistical analysis applied to its time series.
Observation Model. Given that the target equipment is low cost, the complexity of the observation model should be taken into account. The observables were modeled as follows:
ρ is the geometric range between antenna phase centers of receiver and satellite (m),
c is the speed of light in vacuum (m/s),
dT is the receiver clock offset (s),
dt is the satellite clock offset (s),
T is the tropospheric delay (m),
I is the ionospheric delay (m),
λ is the wavelength of the carrier (m),
N is the carrier-phase ambiguity
M, m is the multipath effect on pseudorange and carrier-phase measurements, respectively (m),
ϵP, ϵΦ is the measurement noise and any residual bias for pseudorange and carrier-phase measurements, respectively, including the effect of any dynamics-induced tracking loop errors (m), and
j represents a particular satellite.
The majority of modern mobile platforms have Internet access, and in this research it was assumed that information on satellite orbits, clock offsets and ionospheric delays could be acquired through real-time precise correction streams. For our computations, we used orbits and clocks from the Centre National d’Etudes Spatiales as well as ionospheric delays derived from European Space Agency global ionospheric maps (GIMs). The range term was corrected for Earth tides, ocean loading and relativistic effects.
In our study, coordinate determination is handled with a standard implementation of Kalman filtering. The Kalman filter state vector contains receiver coordinates, receiver clock, carrier-phase ambiguities and tropospheric delay.
Automatic Weight Adjustment. Our study revisited the technique developed by Bisnath and Langley (see Further Reading). First, the pseudo-multipath observable is calculated:
The term 2Ij in Equation (3) can be partially eliminated by applying a GIM correction. The pseudo-multipath observable gives a good representation of code multipath, as the magnitude of the carrier-phase terms in Equation (3) is much smaller than the corresponding pseudorange terms.
Pseudo-multipath observables are stored in a buffer of a size B1 and are used to calculate sample variances for each satellite (see FIGURE 2). When B2 variances are stored in a second buffer, the algorithm has enough data to make a decision as to whether the weights of the observables should be adjusted. The challenging part of the algorithm is the threshold determination, which will be discussed in subsequent sections.
FIGURE 2. Block diagram of the environment detection and weight adjustment algorithm.
TESTING AND RESULTS
We collected an urban dataset consisting of two segments: one stationary and one kinematic. The stationary segment was inspected since in this case the multipath patterns are not randomized by the moving surroundings as in the kinematic segment. When the weighting scheme was developed, we proceeded with its tuning and analyzed its performance in the more challenging, kinematic environment and also added GLONASS observations to the processing.
Preliminary Analysis. First, the behavior of the pseudo-multipath observable during the observation session was analyzed. The initial processing was carried out in GPS-only mode, applying an elevation-angle weighting scheme and 10-degree elevation mask angle. The reference coordinates were obtained with the PPP software developed at UNB using Leica AX1203+ GNSS dual-frequency observations. Thirty-minute static datasets showed that the horizontal error of the coordinates determined with patch antenna observations is just below the 2-meter mark, while the 3D-error is above 5 meters with height error being the biggest contributor (see FIGURE 3).
FIGURE 3. Absolute errors for GPS-only processing, 30-minute static session. Comparison among antennas.
The errors of higher grade antenna datasets proved to be significantly smaller with all error components being below the 0.5-meter mark. The comparison presented in FIGURES 4 and 5 shows a more perturbed behavior of the pseudo-multipath observable in the case of the low-cost patch antenna compared to the Tallysman (static and kinematic parts of the session are presented in the same plot). Interestingly, this behavior is not common for all the satellites tracked; only two of them (G12 and G09) show a high variation in the pseudo-multipath observable and only for periods of time with stable periods in between.
FIGURE 6 illustrates the pseudo-multipath observable compared among three antennas for satellite G12. It shows that, as might be expected, higher grade antennas perform better in terms of multipath rejection. Both G12 and G09 were more than 30 degrees above the horizon and normally would not be excluded from processing. The attempt of applying a weighting scheme based on the carrier-to-noise-density ratio C/N0 did not introduce any accuracy improvement. Indeed, C/N0 values did not show any visible correlation with the illustrated multipath contamination.
FIGURE 6. Pseudo-multipath observables comparison for GPS satellite G12.
We empirically determined that the optimal size of buffer B1 for the 1-Hz low-cost patch antenna data is close to 20 epochs. This value allows the algorithm to trigger adequate increases of variances when the pseudo-multipath observable is perturbed and keep all “good” signals below the calculated threshold. The threshold is determined by statistical analysis of buffer B2 of a reference satellite (see Figure 2).
FIGURE 2. Block diagram of the environment detection and weight adjustment algorithm.
We found it to be a good practice to select the reference satellite as one above 70 degrees elevation angle and with minimal sample variance for low-cost antenna data processing. FIGURE 7 shows the variance behavior for three GPS satellites: calculated statistics allow the algorithm to trigger the adaptive weighting algorithm for multipath-contaminated signals of satellite G12, while G02 and G03 follow the normal elevation-angle-dependent weighting scheme.
FIGURE 7. Pseudo-multipath sample variance comparison among three satellites for the static part of the campaign. Low-cost patch antenna observations.
Static Session. In GPS-only mode, applying the proposed algorithm allowed for a decrease in positioning absolute error for the low-cost patch antenna of more than 50 percent. Horizontal error was brought down to the sub-meter level, while vertical error remained the biggest error contributor being just above 2 meters (see FIGURE 8).
FIGURE 8. Absolute errors for positioning with low-cost patch antenna, 30-minute static session; processing methods comparison.
A comparison of convergence behaviors among the tested antennas and methods for the stationary setup in GPS-only mode indicated the convergence behavior dependency on the applied multipath-rejection efforts. Higher grade antennas capable of reducing multipath to some degree demonstrate much more stable convergence to reference coordinates, while the adaptive weighting algorithm partially eliminates the residual multipath effect at the software level.
As was shown by Lou et al., for example (see Further Reading), single-frequency positioning solutions can benefit from the integration of additional satellite constellations. Here, we report on testing a combined GPS+GLONASS model. For the static case, combined processing outperformed the GPS-only model with adaptive weighting by almost 1 meter in 3D error and improved height estimation by more than 50 percent. The weight adaptation algorithm introduced only a slight improvement in combined processing (see Figure 8).
Kinematic Session. Kinematic standalone positioning is especially challenging in the case of low-cost equipment utilization. The surrounding environment is constantly changing, which is illustrated by a shift in the behavior of the pseudo-multipath observables (see Figures 5 and 6), the C/N0, and the satellite availability.
The reference trajectory for kinematic testing was computed with the Leica AX1203+ GNSS antenna and receiver combination using dual-frequency data with the PPP software developed at UNB. When compared with the reference trajectory, the standard GPS-only solution experiences jumps as large as 9 meters in the horizontal plane and 15 meters in height. Application of the adaptive weighting technique to the same dataset noticeably improves the solution, decreasing the size of jumps in all coordinates (see FIGURE 9).
FIGURE 9. Low-cost patch antenna GPS-only solution superimposed on a georeferenced Google Map: no adaptation (red) and with adaptive weighting (green).
Understandably, the most efficient approach is the additional constellation integration. We estimate that 70 percent of the trajectory was determined with sub-meter horizontal accuracy when the GPS+GLONASS model was used. The adaptive weighting technique showed only minor improvements when applied to the combined model, which brings us to the conclusion that the stochastic model in the proposed algorithm needs to be investigated further.
CONCLUSIONS
Our research experiment allowed us to monitor the performance of low-cost versus high-grade GNSS antennas. The pseudo-multipath observable was shown to be an effective measure to trace the impact of multipath on a navigation signal. Analysis of subsequently calculated variances allowed our algorithm to automatically assess multipath environments and implement an adaptive weighting technique.
The technique proved to be especially effective for use with low-cost patch antenna observations in a GPS-only mode, providing a more than 50 percent increase in accuracy in a static case and noticeable compensations in coordinate jumps in kinematic mode. We intend to further improve the algorithm to potentially make a bigger impact on the combined GPS+GLONASS solution. The automatic adjustment of filtering parameters such as process noise in the Kalman filter can be considered for future research.
ACKNOWLEDGMENTS
Our research is supported by the Natural Sciences and Engineering Research Council of Canada. The authors thank Ryan White at the University of New Brunswick (UNB) for assistance with the observation campaign and Marco Mendonça, also at UNB, for helpful feedback on our work along the way. This article is based on the paper “Adaptive Algorithm for Low-cost Single-frequency Positioning in Urban Environments: Design and Performance Analysis” presented at ION ITM 2018, the 2018 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 29–Feb. 1, 2018.
Ivan Smolyakov is a Ph.D. student in the Department of Geodesy and Geomatics Engineering at the University of New Brunswick (UNB) under the supervision of Richard B. Langley. His research efforts are concentrated on single-frequency precise point positioning challenges.
Richard B. Langley is a professor in the Department of Geodesy and Geomatics Engineering at UNB, where he has been teaching and conducting research since 1981. He has a B.Sc. in applied physics from the University of Waterloo and a Ph.D. in experimental space science from York University, Toronto. Langley has been active in the development of GNSS error models since the early 1980s and has been a contributing editor and columnist for GPS World magazine since its inception in 1990. He is a fellow of The Institute of Navigation (ION), the Royal Institute of Navigation and the International Association of Geodesy. He was a co-recipient of the ION Burka Award for 2003 and received the ION Johannes Kepler Award in 2007.
FURTHER READING
• GPS and Multi-GNSS Single Receiver Positioning
“Multi-GNSS Precise Point Positioning with Raw Single-frequency and Dual-frequency Measurement Models” by Y. Lou, F. Zheng, S. Gu, C. Wang, H. Guo and Y. Feng in GPS Solutions, Vol. 20, No. 4, October 2016, pp. 849–862, doi: 10.1007/s10291-015-0495-8.
“Intelligent Urban Positioning using Multi-Constellation GNSS with 3D Mapping and NLOS Signal Detection” by P.D. Groves, Z. Jiang, L. Wang and M.K. Ziebart in Proceedings of ION GNSS 2012, the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, Sept. 17–21, 2012, pp. 458–472.
“Multiple Faulty GNSS Measurement Exclusion Based on Consistency Check in Urban Canyons” by L.-T. Hsu, H. Tokura, N. Kubo, Y. Gu and S. Kamijo in IEEE Sensors Journal, Vol. 17, No. 6, March 15, 2017, pp. 1909–1917, doi: 10.1109/JSEN.2017.2654359.
“Robust Outlier Mitigation in Multi-Constellation GNSS Positioning for Waterborne Applications” by J.A. Pozo-Pérez, D. Medina, I. Herrera-Pinzón, A. Heßelbarth and R. Ziebold in Proceedings of ION ITM 2017, the 2017 International Technical Meeting of The Institute of Navigation, Monterey, California, Jan. 30 – Feb. 2, 2017, pp. 1330–1343.
“Least-Squares Estimation and Kalman Filtering” by S. Verhagen and P.J.G. Teunissen, Chapter 22 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.
Makers of the mobile GIS app SuperSurv, developed by Supergeo Technologies Inc., are working to increase its GNSS positioning functionality.
In recent weeks, the SuperSurv product team began to enhance SuperSurv’s NTRIP solution, aiming to adopt more RTCM versions and provide a better GNSS positioning service. NTRIP (Networked Transport of RTCM via internet protocol) is a protocol to send GNSS-related data through the internet, which enables users of differential GPS or network RTK to get correction parameters after connecting to the internet. The correction parameters can be used to calculate a more accurate GNSS location.
Supergeo’s product team is developing the support for RTCM 3.1, including Type 1021 and 1023, two kinds of messages. Type 1021 contains the seven parameters for 3-axis coordinate transformation — three for 3-axis translation, another three for 3-axis rotation and a scale factor.
Through the original projection method, users can only get rough coordinates. However, with NTRIP solution, users can send the current location to the server and then receive the parameters provided by it. This makes it easier to obtain a suitable local coordinates frame for more precise coordinates.
The Type 1023 message provides more accurate grid residuals. By establishing a 4 x 4 mask window around the rover, users will receive the 3-axis corrections within these 16 grids. Accordingly, a more accurate GNSS positioning is achievable after interpolation.
Image: SuperSurv
After completing the development, this technique will be implemented in the current version, SuperSurv 10.1. Combined with SuperSurv’s existing GIS features, Supergeo believes the newly supported RTCM 3.1 will bring a brand-new experience to fieldworkers.
Launched in November 2017, current version 10.1 offers three major new features, including snapping, coordinate system customization and Layerset. Google Maps and TIFF are also supported.
Supergeo’s product manager for mobile GIS, Zara Yu, recommend that users activate the point-data auto-collection with RTCM 3.1. This method not only helps users skip repeated operations but also enhances the data quality and efficiency.
Harxon has introduced an advanced, high-speed, Bluetooth-enabled wireless rover radio.
The HX-DU1603D, designed for GNSS/RTK surveying and precise positioning, will be showcased this September at the Intergeo trade show in Berlin, Germany.
The HX-DU1603D is a lightweight, ruggedized UHF receiver designed for data communications between 410 MHz and 470 MHz in either 12.5 KHz or 25 KHz channels, which can be widely used in GNSS/RTK surveying and GNSS precise positioning fields.
It is equipped with a Bluetooth transceiver for wireless communications with external devices. It features a 6800 mAh rechargeable internal battery and configurable transmit power between 0.5W and 2W. Its IP67 waterproof capability allows long operating hours outdoors, the company said.
The HX-DU1603D rover radio is easy to operate and use. It is equipped with a 1.9-inch display screen that supports frequency, protocols, power display, serial port baud rate and air baud rate. By deploying these technologies, users can instantly communicate with GNSS precise positioning receivers with the same protocols throughout the world.
The rover radio HX-DU1603D has joint Harxon product lines, including 25W base radio HX-DU8602T with simplex and 35W base radio HX-DU8608D with duplex.
Future automotive GNSS positioning in urban scenarios
By Martin Escher, Mirko Stanisak and Ulf Bestmann
INNOVATION INSIGHTS with Richard Langley
WHERE ARE WE GOING with GNSS positioning? There have been many advances in satellite-based positioning over the past couple of decades and there are more to come.
Probably the most significant advance, affecting the most users, has been the further miniaturization of GNSS chipsets and modules. Virtually every mobile phone now includes a GPS component. Developers have also made these embedded devices more sensitive so that they can work with smaller, less efficient antennas. Furthermore, GPS satellites are now being launched with additional, more capable signals and already high-end receivers are starting to use these signals. Once full constellations transmitting these signals are in place, consumer devices will likely make use of them as well.
Another very important advance in GNSS positioning has been the development of additional GNSS constellations and multi-GNSS receivers capable of using their signals. Actually, it’s been a multi-GNSS world for quite a while now. The Russians began development of GLONASS shortly after work began on fielding GPS and both systems achieved full operational capability in the mid-1990s. Unfortunately, due to financial problems following the break-up of the Soviet Union, the number of operating GLONASS satellites fell to the single digits making the system virtually unusable. However, with renewed government support, GLONASS has once again become a viable GNSS and many consumer and professional receivers can track and use GLONASS signals along with those of GPS.
In the 1990s, we also saw the development of the U.S. Wide Area Augmentation System, transmitting GPS correction and integrity information from geostationary satellites on the GPS L1 (and subsequently L5) frequency. Other compatible satellite-based augmentation systems followed, including the European Geostationary Navigation Overlay Service, Japan’s Multi-Functional Transport Satellite Satellite-based Augmentation System, India’s GPS Aided GEO Augmentation System, and Russia’s System for Differential Correction and Monitoring. Besides enhancing integrity, the data transmitted by the satellites of these systems improves GPS pseudorange-based positioning accuracy, sometimes to below the one-meter level.
Starting about 15 years ago, we have seen the development of additional autonomous GNSSs, joining GPS and GLONASS. The European Galileo system is under construction as is China’s BeiDou system. And although only providing regional coverage, we should also mention Japan’s Quasi-Zenith Satellite System and the Indian Regional Navigation Satellite System. While all of the new systems are still in development and full constellations are still some years away from completion, the signals from the satellites already in orbit can be used to supplement those received from GPS and GLONASS satellites to improve positioning and navigation availability for some difficult navigation scenarios.
One of the most difficult situations requiring a continuous positioning capability is driving in built-up areas where buildings and other objects can block the signals from a number of GPS satellites such that GPS-only positioning becomes impossible. Even if four or more satellites are in view of the satellite navigation receiver’s antenna, those satellites might have unfavorable geometry, resulting in significantly degraded positioning accuracy. However, if the receiver can access the signals of two or more GNSSs, then position fixes might be available where none were possible with GPS alone, and the accuracies of marginal fixes might be improved.
In this month’s column, we take a look at some early work in using multi-GNSS plus additional sensors for navigating in the heart of the city of Braunschweig, Germany (the birth place of Johann Friedrich Carl Gauss, the inventor of least squares and the father of modern geodesy), and how the additional signals can help us to get where we’re going.
In the near future, we will see the introduction of more and more next-generation advanced driver assistance systems (ADASs) targeting the field of automated or autonomous driving. These systems will have to be considered as safety critical. In contrast to conventional localization systems, they will have to guarantee a higher overall accuracy and integrity to their target applications. Of course, the overall performance of any localization system is mostly limited by its behavior during the worst conditions.
Such behavior is a very limiting factor especially for an ADAS that uses a GNSS such as GPS. The accuracy and integrity of GNSS depend on the quality and availability of satellite signals. The more signals that are available, the greater are the accuracy and integrity. However, as GNSS signals can be blocked easily, the worst-time behavior is difficult to characterize, especially in challenging urban scenarios important for an ADAS.
To face these challenges, additional sensors such as inertial measurement units (IMUs) or odometers can be used for positioning as well. These sensors can increase the availability and accuracy for situations where GNSS-based positioning is not available. Additionally, the characteristics of these sensors are complementary to satellite navigation. The combination of these sensors with satellite navigation thus has the potential to achieve a positioning accuracy and integrity superior to that of single-system performance.
As the number of GNSS measurements is crucial for a precise position fix, the parallel use of different GNSS constellations can improve the overall performance significantly.
Four global satellite-positioning systems are now available. The American GPS and the Russian GLONASS have been in operation for years and are already used in a wide variety of applications. Additionally, newer systems like the European Galileo and the Chinese BeiDou systems are under construction. Even though these systems do not have continuous worldwide availability at the moment, their currently available satellites can already be included in multi-constellation GNSS positioning. Once more satellites are in orbit, the benefit of multi-constellation GNSS will increase even further.
In this article, we take a look at the current performance of multi-constellation GNSS positioning in an urban scenario, contrasting it with GPS-only positioning as well as GNSS positioning aided by additional sensors.
Satellites in orbit
To characterize multi-constellation GNSS performance, stationary GNSS data has been collected using different receivers in Braunschweig, Germany. GNSS data from GPS, GLONASS, Galileo and BeiDou was recorded over a 14-hour window on November 20, 2015.
Based on the broadcast ephemeris data and the receiver’s position, the availability of GNSS measurements was calculated for the duration of the campaign. TABLE 1 shows the number of all satellites of the different constellations as well as the minimum and maximum number of available satellites for each system during the recording period down to an elevation angle of 0°.
Table 1. Number of satellites in orbit and in view during a 14-hour window.
FIGURE 1 shows the satellite availability for the measurement campaign. To obtain a position fix using a single GNSS constellation, range measurements to at least four satellites of this constellation must be acquired. Thus, assuming optimal reception of GNSS signals, single-constellation positioning was possible for the full observing window using only GPS, only GLONASS and only BeiDou satellites. On the other hand, Galileo-only position fixes were not possible at any time due to the low number of simultaneously visible satellites.
FIGURE 1. Satellites in view from Braunschweig, Germany.
However, combining measurements from different GNSS constellations in parallel — multi-constellation GNSS — provides the most benefit.
Multi-Constellation GNSS
All major GNSS constellations operate independently and use different reference frames for position and time. To combine measurements of different GNSS constellations, the correct handling of the diverse reference frames needs to be ensured.
On the one hand, the different coordinate systems have to be taken into account. However, the differences between the position frames is usually kept to within a few centimeters and can thus be neglected for most standalone-GNSS applications.
On the other hand, the handling of the different system time scales requires a specific approach. Even though the inter-system biases (that is, the differences between the system time scales) are usually kept within a few nanoseconds, the influence of the inter-system offsets must not be ignored for most applications and have to be taken into account for a combined position solution.
The most common approach is to extend the estimated state vector with a distinct clock error for each used constellation. For a combined position solution incorporating GPS, GLONASS, Galileo and BeiDou, the state vector used for the least-squares estimation could look like this:
. (1)
Each pseudorange measurement only contributes to its respective clock-error component.
Of course, as the values of more unknown variables have to be estimated, the number of necessary GNSS measurements increases, too. To calculate a combined position solution including GPS, GLONASS, Galileo and BeiDou for the above-mentioned example, seven variables must be estimated. This means that at least seven independent GNSS measurements are necessary at each epoch. However, if no satellite of a specific constellation is available, the state vector can also be adapted to not estimate the corresponding clock error. In this way, the availability of a multi-constellation GNSS solution is always higher or, in the worst case, equal to that of the single-constellation GNSS solutions.
By being able to use more than just one GNSS constellation, the geometric distribution of the satellites over the sky is improved, resulting in a lower dilution of precision (DOP). A lower DOP value usually indicates a better mapping of range measurement precision into the position precision. However, as the different GNSS constellations are currently in different states of maturity, the range precision varies significantly. Thus, a multi-constellation position solution is not necessarily more accurate than a single-constellation solution, but will benefit from an improved overall availability and integrity.
Such a capability is particularly important for safe operations in constrained scenarios like urban canyons, which are a common challenge for automotive applications. Compared to currently prevailing GPS-only positioning, multi-constellation GNSS has the potential to enable safety-of-life services, which will require a high level of integrity in the automotive domain.
Tight coupling
To take even greater advantage of multi-GNSS positioning in challenging environments, the combination with additional sensors can improve the overall positioning performance significantly. The Institute of Flight Guidance at the Technische Universität Braunschweig has developed a tightly coupled GPS fusion system, which incorporates measurements of a close-to-market IMU and odometer sensors for reliable urban car positioning.
This system is capable of using raw data from a reference station receiver to generate differential GNSS corrections. These differential corrections must be free from reference-receiver clock error before they can be used by the tightly coupled system (rover-receiver clock-bias update by pseudorange positioning, rover-receiver clock-drift update by Doppler frequency velocity estimation, and clock-bias prediction by clock drift).
. (2)
As shown in Equation 2, the system calculates the residuals for each pseudorange (PSR) received by the reference receiver based on the well-known reference antenna position and the current satellite position as calculated using its broadcast ephemeris . While calculating the residuals, it involves the atmospheric effects ε j computed by the Klobuchar ionosphere delay model and a modified Hopfield tropospheric delay model.
These residuals must be corrected by the satellite clock errors (also calculated using the broadcast ephemeris). The arithmetic average of the corrected residuals is used as an estimate for the reference receiver clock error (see Equation 3). This approach is sufficient for most applications, but it is also possible to use additional algorithms to estimate the clock error more accurately.
. (3)
To generate reference receiver clock error-free pseudorange corrections, the residuals are calculated a second time. Only the estimated clock error of the reference receiver is removed in the second set of residuals:
. (4)
The assumption was made that these residuals correct all satellites, all atmospheric errors and the inter-system time errors.
With this assumption, the tightly coupled system uses the corrected residuals as pseudorange corrections for the ranges measured by the rover receiver. Using the corrected pseudoranges, the tightly coupled system can estimate the rover receiver’s clock error for positioning:
. (5)
In this way, the inter-system offsets are eliminated as well. Corrected multi-constellation GNSS measurements can thus be processed by estimating one receiver clock error only.
Simulation of obstacles
The performance of satellite navigation is affected directly by the distribution of the useable GNSS satellites over the sky. The more GNSS satellites are spread out over the sky, the lower the DOP value and the better the positioning accuracy. For reference, FIGURE 2 shows a sky plot of unconstrained GNSS with perfect reception of all GNSS satellites during the measurement period of 14 hours. Combining the satellites of all four GNSS core constellations (GPS, GLONASS, Galileo and BeiDou), up to 30 satellites are usable at the same time.
FIGURE 2. Sky plot of GNSS satellites (GPS, GLONASS, Galileo and BeiDou) at Braunschweig.
Of course, this is an optimized scenario that can only be achieved using high-quality antennas without any obstacles in the vicinity. Many applications, including urban automotive situations, do not have a comparable reception of GNSS data, and will suffer from blocked satellites and multipath reception.
Therefore, we created a simulation of surrounding obstacles to predict the behavior of GNSS positioning in challenging urban scenarios. In this simulation, all buildings are represented by endless walls with constant height. A satellite is assumed to be invisible if its line of sight crosses the wall.
To get a first impression of the usability of this approach, we took GNSS measurements in front of the Institute of Flight Guidance in Braunschweig.
Using this scenario, the same simulation of optimal visibility using ephemeris data has been conducted again. As shown in FIGURE 3, large portions of the sky are blocked by the simulated obstacles.
FIGURE 3. Sky plot with valid (thick lines) and invalid (thin lines) measurements.
Of course, the blockages also affect the number of visible satellites as shown in FIGURE 4. Instead of 23 to 31 satellites for the unconstrained scenario, only 11 to 18 satellites are now visible.
FIGURE 4. Comparison of satellite visibility with and without simulated obstacles.
In a following step, we validated the theoretical predictions of the visible GNSS satellites against the reception by a GNSS receiver of the available signals at the simulated position.
Validation of simulation
For a validation of the obstacle simulation, data from a high-grade receiver was used for the validation of the simulation. This modern GNSS receiver is able to track signals from all GNSS constellations (GPS, GLONASS, Galileo and BeiDou) on different GNSS frequencies with a data rate of up to 100 Hz. The BeiDou reception, however, was only acquired recently before the recording of the data and unfortunately suffered from bad BeiDou tracking performance.
The receiver was connected to a multi-frequency antenna. This GNSS antenna was installed at the back of the roof of the research car. A sky plot of the tracked signals is shown in FIGURE 5.
FIGURE 5. Tracked signals of the high-end receiver.
A comparison of the simulated (Figure 3) and the actual (Figure 5) sky plots shows a very good agreement between the simulations and the measurements. There are, however, some spots in the sky plot where the real GNSS receiver is able to track satellites that are behind a building. This can be explained by the reception of signals through the windows of the building. Thus, the signal-quality indication based on the receiver’s signal-to-noise measurements of these spots is quite bad in these situations.
As described before, we experienced some problems with the BeiDou reception of the high-grade receiver. Thus, we used an additional single-frequency GNSS receiver. This receiver is capable of providing raw L1 GNSS data of two constellations simultaneously and was configured to track GPS and BeiDou satellites. In this way, an additional sky plot showing GPS and BeiDou reception in the same setup could be generated. The visible BeiDou satellites are shown in light blue in FIGURE 6 and are in accordance with the simulated visibility.
FIGURE 6. Valid signals sky plot of the single-frequency receiver data.
In general, the sky plots identify significant differences compared to the simulated ones as even in regions blocked by buildings some satellites can still be tracked. The contour of the building, however, can still be seen in the signal strength plot in FIGURE 7.
FIGURE 7. Signals strength sky plot of the single-frequency data.
This result is an indication that the single-frequency receiver can track some satellites blocked by the buildings using diffracted or reflected signals, but, of course, resulting in worse positioning accuracy.
It goes without saying that the various receivers we used are designed with contrary goals in mind. High-performance GNSS receivers are optimized to provide accurate position solutions for high-demanding applications. Thus, the receiver attempts to suppress multipath effects as much as possible to obtain precise and accurate position solutions. The single-frequency receiver, on the other hand, is closer to the low-price, high-volume class of receivers for portable devices, and is optimized to provide valid position output even in challenging environmental situations. Thus, the receivers must not be compared directly because they are designed for completely different purposes.
Simulating urban canyons
To assess the overall multi-GNSS performance in urban scenarios, we conducted driving tests in the city center of Braunschweig. Driving through city centers is particularly challenging for any positioning algorithm because of various potential sources of errors. Instead of only using suburban commuter roads, the route we chose represents the most challenging situations for the city center. Most of the roads are surrounded by multi-story buildings (typically up to six floors) very close to the driving lanes. This is – especially for European cities – a common and challenging urban scenario for satellite navigation. An example of such a scenario is shown in FIGURE 8.
FIGURE 8. Dimensions of representative urban scenario.
To quantify the impact of the limited GNSS availability due to buildings and other obstacles, we simulated a scenario with walls on both sides of the road. With the road running in a north-south direction, we simulated buildings within a distance of 14 meters and a height of 15 meters. The simulated effect on a GNSS receiver in the middle of the street due to blocked satellites in this scenario is shown in FIGURE 9. Satellites with an elevation angle of up to 65° can be obstructed by the buildings.
FIGURE 9. Sky plot for obstacle simulation of urban canyon.
In this scenario, more than half of the sky is blocked by buildings, making satellite navigation quite challenging. Additionally, Braunschweig is located at about 52° north latitude and is close to the inclination of most GNSS constellation orbits (GPS 55°, Galileo 56°, BeiDou MEO 55°). Only GLONASS satellites can be seen in the far northern part of the sky from time to time due to their inclination of 65°.
Using GPS satellites only, fewer than four satellites are available for long periods of time. On the other hand, using a combination of all constellations, up to 14 satellites can be used even for this constraining scenario. Most of the time, at least seven satellites are visible, allowing a multi-constellation GNSS position solution.
Downtown positioning
To assess the practical benefit of multi-constellation GNSS in urban scenarios, we conducted a test drive in downtown Braunschweig using our research car. This area is dominated by narrow roads with multi-story buildings on both sides of the road. Using recorded data from different GNSS receivers and other sensors, multiple positioning solutions were obtained by post-processing the recorded data to compare the different positioning performances.
As a baseline for comparison, a GPS-only position solution was calculated. This result represents the current state-of-the-art navigation systems for most production cars. All valid GPS-only position fixes are shown in FIGURE 10. For large portions of the test drive, no GPS-only position solution was possible because of insufficient GPS measurements.
FIGURE 10. GPS-only standalone positioning fixes for test drive in Braunschweig.
To quantify the benefit of multi-constellation GNSS compared to GPS-only, a combined position solution was calculated using the same data as before. There was a significant improvement in the availability compared to the GPS-only position solution.
However, even when using multiple GNSS constellations, some areas with no valid GNSS fixes still exist. The GNSS availability can be improved further by using differential corrections from a GNSS reference receiver. The correction data is available in the research car using 4G mobile telecommunication links to different service providers. Each provider uses a network of GNSS receivers to calculate differential corrections. However, all commercially available services are currently limited to GPS and GLONASS. Thus, another stationary multi-constellation GNSS reference receiver at the Institute of Flight Guidance generated correction data for the test drives. As the baselines are short in this scenario (not longer than 10 kilometers), no significant spatial decorrelation is expected.
As the majority of possible inter-system offsets are already eliminated using the differential corrections of identical receiver types, a multi-constellation solution can be calculated here even with as few as four GNSS satellites in view. This is shown in FIGURE 11. In this way, the achieved availability increased again.
FIGURE 11. Differentially corrected multi-constellation positioning fixes for test drive in Braunschweig.
Finally, using all the information available in the car, a hybrid position solution based on differentially corrected GNSS, inertial navigation and the test vehicle’s odometer has been calculated.
In sections without any GNSS positioning aiding (marked red in FIGURE 12), the inertial navigation system was used in dead-reckoning mode. As these outages lasted only for short periods of time, the accuracy of the combined position remained usable for the duration of the test. In this way, an accurate position solution could be calculated for the whole test drive using this tightly coupled positioning algorithm.
FIGURE 12. Tightly coupled positioning trajectory for test drive in Braunschweig.
With increasing positioning complexity, the computational burden increased as well. For a tightly coupled system integrating the measurements of the different sensors, significantly more calculations must be performed in real time than for current GPS-only standalone positioning. However, even today these computations can be easily made using embedded devices.
Conclusions and outlook
For this article, the achievable positioning performance of multi-constellation GNSS has be analyzed with a special emphasis on urban automotive applications. Simulations of constrained environments have been compared with real data and show good agreement. Multi-constellation GNSS outperforms GPS-only positioning, especially in situations where large portions of the sky are blocked by obstacles, because significantly more satellites remain usable. Multi-constellation GNSS has thus the potential to be an important part of future safety-of-life positioning and navigation applications.
However, a few challenges still exist. Some GNSS constellations have not reached their full operational capabilities as not all satellites are in orbit yet (Galileo and BeiDou). Additionally, the ranging errors of these systems are expected to decrease with improved navigation message accuracy and receiver performance.
The availability of numerous GNSS constellations results in new requirements for the receivers as well. Even though most manufacturers of GNSS equipment already support the additional systems with some products, the majority of currently used GNSS receivers is limited to one or two constellations, especially in mass-market applications. In addition, the reception quality of the newer systems is not always on the same level as GPS or GLONASS because of the limited experience that manufacturers have with Galileo and BeiDou. This, we hope, will change in the near future.
Acknowledgments
This article is based on the paper “Future Automotive GNSS Positioning in Urban Scenarios” presented at The Institute of Navigation 2016 International Technical Meeting, held in Monterey, Calif., Jan. 25–28.
Manufacturers
The high-grade receiver used in our tests was a Septentrio AsteRx3. The receiver was connected to a NovAtel GPS-703-GGG antenna. The single-frequency receiver we used was a u-blox LEA-M8T GNSS receiver with firmware version 2.3. Additionally, we used a NovAtel OEM6 multi-GNSS receiver and an Analog Devices ADIS16375BMLZ IMU.
MARTIN ESCHER holds a Dipl.-Ing. in electrical engineering from the Technische Universität (TU) Braunschweig in Braunschweig, Germany, and has been employed as a research engineer at the Institute of Flight Guidance (IFF) since 2010.
MIRKO STANISAK is a research assistant and Ph.D. candidate at the IFF of TU Braunschweig. He received his Dipl.-Ing. in mechanical engineering in 2009 and since then has worked on various GNSS-related topics.
ULF BESTMANN received his Dr.-Ing. in mechanical engineering from the TU Braunschweig in 2010. He is employed at the IFF of TU Braunschweig, where he is head of the navigation department.
Further Reading
Authors’ Conference Paper
“Future Automotive GNSS Positioning in Urban Scenarios” by M. Escher, M. Stanisak and U. Bestmann in Proceedings of ITM 2016, the 2016 International Technical Meeting of The Institute of Navigation, Monterey, Calif., Jan. 25–28, 2016, pp. 836–845.
Multi-Constellation GNSS Measurements
“Precise Point Positioning with Galileo Observables” by R.M. White and R.B. Langley in Proceedings of the 5th International Colloquium on Scientific and Fundamental Aspects of the Galileo Programme, Braunschweig, Germany, Oct. 27–29, 2015.
“Accuracy and Reliability of Multi-GNSS Real-Time Precise Positioning: GPS, GLONASS, BeiDou, and Galileo” by X. Li, M. Ge, X. Dai, X. Ren, M. Fritsche, J. Wickert and H. Schuh in Journal of Geodesy, Vol. 89, 2015, pp. 607–635, doi: 10.1007/s00190-015-0802-8.
“Performance Evaluation of Integrated GPS/GIOVE Precise Point Positioning” by W. Cao, A. Hauschild, P. Steigenberger, R.B. Langley, L. Urquhart, M. Santos and O. Montenbruck in Proceedings of ITM 2010, the 2010 International Technical Meeting of The Institute of Navigation, San Diego, Calif., Jan. 25–27, 2010, pp. 540–552.
“A GPS/Galileo Tightly-Coupled Localization System for Safety-Relevant Automotive Assistance Systems” by H.-G. Büsing, M. Escher, T. Scheide and P. Hecker in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Ore., Sept. 19–23, 2011, pp. 356–362.
Geometry Effects on GNSS Positioning
“Dilution of Precision” by R.B. Langley in GPS World, Vol. 10, No. 5, May 1999, pp. 52–59.
The 24th edition of the European Navigation Conference (ENC 2016) will be held May 30 to June 2 at the Finlandia Hall in Helsinki, Finland.
ENC 2016 is co-sponsored by EUGIN, Nordic Institute of Navigation, IEEE Aerospace and Electronic Systems Society.
The conference focus will be on innovations in positioning, navigation and timing technologies and applications for land, sea and air.
Topic areas include GNSS positioning, indoor and urban navigation and position-based applications. Special topics include navigation challenges in the Arctic and positioning solutions using geospatial big data and in intelligent transportation. Furthermore, it promises to be a unique networking event for all participants from academia, the public sector, and industry.
Welcome keynotes will be presented by Anne Berner, Finland’s minister of Transport and Communications, Matthias Petschke, director for European Satellite Navigation Programmes, European Commission, and Tiina Tuurnala, deputy director general, Finnish Transport Agency.
Technical keynotes will be given by Jari Syrjärinne, HERE Ltd., and Gérard Lachapelle, University of Calgary. The closing keynote by Prof. John Raquet, Air Force Institute of Technology. The conference will feature also technical presentations, panels, posters and an industry exhibition.
The social program of ENC 2016 will showcase unique sights that Helsinki has to offer, including the ice-breaker evening onboard the actual ice-breaker vessel Urho and performances by a traditional Finnish Kantele musician.
The preliminary program is now available, and registration is open. Registration options include fees for the whole conference as well as for individual days.
SkyTraq Technology Inc., developer of high-performance chipset and module solutions for the GNSS market, unveiled its new S2525F8-RTK low-power, single frequency RTK receiver for applications requiring centimeter-level accuracy positioning.
S2525F8-RTK is a multi-constellation GNSS RTK receiver that can use 12 GPS, eight SBAS, six BDS, and one QZSS signal. In situations where an RTK fix is not possible, a Float RTK mode can be used for decimeter-level accuracy positioning.
A moving-base mode supports a precise heading GPS compass application. The receiver is 25 millimeters by 25 millimeters form factor, weighs 3 grams and consumes 250 milliwatts of power for any outdoor mobile applications requiring high precision RTK positioning, SkyTraq reported in a news release.
S2525F8-RTK supports both base station and rover modes. As a rover, it receives RTCM (Radio Technical Commission for Maritime Services) 3.0 or 3.1 data from a base station — or raw measurements from another S2525F8-RTK receiver serving as base station — and performs carrier phase RTK processing to achieve relative positioning with 1 centimeter plus 1 part-per-million position accuracy with 10-kilometer baseline. Decimeter-level accuracy for over 10-kilometer baseline can be achieved using the Float RTK mode. Two S2525F8-RTK receivers can be used to form a GPS compass that provides better accuracy and more reliable heading solution than conventional digital magnetometers that’s affected by changes in the magnetic environment, according to SkyTraq.
A $50 NS-HP evaluation board is available for evaluation and integration into portable survey equipment, unmanned vehicles, machine control applications and robotic guidance applications. The standard NMEA-navsat-driver package of Robot Operating System (ROS) works directly with NS-HP, enabling accessible centimeter-level accuracy positioning for robotic applications, SkyTraq says.
Topcon Positioning Group has released a new excavator system with local positioning system (LPS) capabilities. The X-53i LPS is designed to provide a solution for machine-controlled excavation in sky-obstructed areas.
“The system is perfect for projects such as tunnel construction or working within existing structures using a total station and prism for precision,” said Kris Mass, director of construction product management. “It’s also versatile when GNSS positioning is available with the new Topcon MC-i4 receiver. Operators can easily choose which type of sensor to best use for the project.”
The system is compatible with the new Topcon GX-55 control box — a large sunlight-viewable LCD touchscreen with integrated LED light bars designed for continuous grade reference of the bucket’s teeth. “It’s the finest graphical experience for modern machine control with customizable audible tones, all wrapped up in a lightweight package for easy transfer and storage,” Maas said.
. While calculating the residuals, it involves the atmospheric effects ε j computed by the Klobuchar ionosphere delay model and a modified Hopfield tropospheric delay model.
