Tag: autonomous vehicles

Future automotive GNSS positioning in urban scenarios

By Martin Escher, Mirko Stanisak and Ulf Bestmann

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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.

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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°.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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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.

“Getting a Grip on Multi-GNSS: The International GNSS Service MGEX Campaign” by O. Montenbruck, C. Rizos, R. Weber, G. Weber, R. Neilan and U. Hugentobler in GPS World, Vol. 24, No. 7, July 2013, pp. 44–49.

“Precise Positioning with Galileo Prototype Satellites: First Results” by R.B. Langley, S. Banville and P. Steigenberger in GPS World, Vol. 23, No. 9, Sept. 2012, pp. 45–49.

“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.

“The Future Is Now: GPS + GNSS + SBAS = GNSS” by L. Wanninger in GPS World, Vol. 19, No. 7, July 2008, pp. 42–48.

• Tightly-Coupled GPS Fusion System

“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.