Building obstructions and reflections present

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1 WITH RICHARD B. LANGLEY REFLECTED BLESSINGS Position Estimation Using Non-Line-of-Sight Signals By Yuting Ng and Grace Xingxin Gao Building obstructions and reflections present serious challenges to receivers operating in urban environments. In such environments, buildings may obstruct signals, leading to reduced signal availability. In addition, buildings may reflect signals, resulting in reception of nonline-of-sight (NLOS) signals. NLOS signals are delayed versions of the line-of-sight (LOS) signals. As such, they lead to pseudorange errors, resulting in positioning errors. Conventional approaches treat NLOS signals as unwanted interference to be rejected or mitigated. Conventional approaches reject NLOS signals at multiple stages of signal processing. Antenna-based approaches include the use of right-hand-circularlypolarized (RHCP) antennas and controlled reception pattern antennas (CRPA). Correlator-based approaches include the use of the narrow correlator, the doubledelta correlator, the multipath estimating delay lock loop (MEDLL) and the vision correlator by various receiver manufacturers. In addition, receiver autonomous integrity monitoring (RAIM) approaches reject pseudoranges with inconsistent positioning residuals. Besides rejecting NLOS signals, conventional approaches also make use of robust filtering and joint signal tracking techniques to mitigate the effects of these signals. Robust filtering techniques include the use of Bayesian filters such as Kalman filters and particle filters. Joint signal tracking techniques include vector tracking and direct position estimation (DPE). A list of existing approaches addressing NLOS signals is provided in TABLE 1. In contrast to conventional approaches that reject or mitigate the effects of NLOS signals, we propose transforming NLOS signals from being unwanted interference to becoming additional useful navigation signals. In addition, we provide a navigation solution under reduced signal availability. RELATED WORK In our approach to using NLOS signals, we make use of DPE and 3D map-aided positioning. The following sections provide an overview of these techniques. Direct Position Estimation. DPE is an unconventional joint signal tracking and navigation technique that directly estimates the receiver s navigation parameters from the raw signal. It does so by directly comparing the expected signal reception of multiple potential navigation candidates against the actual received signal. The navigation solution is then estimated as the navigation candidate with the highest overall correlation between the expected and the actual received signal. This overall correlation is an accumulation of signal correlations across all available satellites, with replica signal parameters aligned to the candidate navigation parameters. In this manner, DPE jointly uses signal correlations from all available satellites to produce a robust navigation solution. 3D Map-Aided Positioning Techniques. State-of-the-art approaches use available 3D maps to predict NLOS signal reception. Apart from rejecting and/or mitigating the effects of NLOS pseudoranges, state-of-the-art approaches leverage the benefits of NLOS pseudoranges, constructively using the affected pseudorange measurements through special treatment of NLOS paths during trilateration. Using 3D building models, they model NLOS paths as LOS paths from satellites to virtual receivers located at receiver mirror-image positions. However, these approaches are limited by the issue of reduced signal Reject Antenna RHCP CRPA Correlator Narrow Double-delta MEDLL Vision correlator RAIM Mitigate Bayes filtering Kalman filter Particle filter Joint signal tracking Vector tracking Direct position estimation (DPE) TABLE 1 Approaches for rejecting and mitigating NLOS signals. 42 WORLD MARCH 2017

2 Arume vernatem. Nam res sim dolupta tiundig niminciditae volupti umquamus untia volut vent ea quas solo velitia earibusae Nam ut explique voluptat quis reiciendem doluptate vel invellis. INNOVATION INSIGHTS BY RICHARD B. LANGLEY THIS ARTICLE IS ABOUT VIRTUAL SATELLITES. No, we don t mean physical objects that are almost satellites. That s the common everyday meaning of the word virtual. We mean it in the sense used in computing to describe something that is not physically present but made to appear so by software (and perhaps aided by hardware). The word was first used in this sense by computer scientists in the 1950s in the term virtual memory to describe a memory management technique. It is now widely used in computing, most commonly as virtual reality. But what is a virtual satellite then? As we all know, satellite signals are quite weak. The antenna of a standard receiver needs to have a clear line-of-sight (LOS) view to the satellites for successful signal tracking and position determination. Buildings and other structures will block signals coming from certain directions. In built-up areas, this can result in fewer LOS signals than the minimum of four needed for unaided positioning. Even with four or more LOS signals, the receiver-satellite geometry may be poor resulting in a large dilution of precision and poor positioning accuracy as a result. It is true that augmentations such as wheel sensors and inertial measurement units coupled with dead reckoning may permit an acceptable level of positioning accuracy for some kinematic applications, but the accuracy will degrade over time if satellite blockage continues unabated. And yes, multi-gnss can help in these situations with receivers availing themselves of additional LOS signals from the GLONASS, Galileo, and BeiDou systems and in Japan, QZSS. But Galileo, BeiDou and QZSS are still in development with a variable number of satellites available at a given location during the day. Is there anything else that can be done to improve the availability of signals? In fact, there are often more signals arriving at a receiver s antenna than just the LOS signals. These are non-line-of-sight (NLOS) signals that bounce off nearby structures before arriving at the antenna. We call the phenomenon multipath and, as we have discussed before in this column, multipath typically reduces positioning performance when the NLOS signals from a particular satellite combine with the LOS signal to distort a receiver s standard correlator outputs thereby biasing pseudorange and carrier-phase measurements. Various techniques have been developed to reject multipath signals at the antenna or in the receiver while others have been developed to lessen the effect of these signals and so minimize their impact on position solutions. On the other hand, non-positioning applications have been developed to use reflections from the Earth s surface to measure snow depth, ground moisture content, and ocean-surface roughness. But could we somehow use multipath signals to improve positioning applications rather than degrade them? In this month s column, we look at a technique developed by researchers at the University of Illinois at Urbana-Champaign that distinguishes a reflected NLOS signal of a particular satellite from the LOS signal and characterizes the NLOS signal as coming from a virtual mirror-image satellite in the direction of the signal reflection point. By using information on the position and orientation of the reflector, the NLOS signal can be treated as an additional LOS signal, albeit from a ghost satellite. The authors have demonstrated that the technique works well in practice and in one difficult positioning environment, obtained an improvement in horizontal position accuracy of 40 meters a reflected blessing indeed. MARCH WORLD 43

3 (a) Non-line-of-sight (NLOS) (b) Virtual satellite (mirror-image position) Building reflection Line-of-sight (LOS) NLOS signal becomes LOS signal to virtual satellite Line-of-sight (LOS) antenna antenna FIGURE 1 NLOS signal transformed from being (a) an unwanted interference to becoming (b) an additional LOS signal to a virtual satellite at the satellite mirror-image position. availability due to multipath fading in addition to building obstruction. Under reduced signal availability, the navigation solution obtained via trilateration is degraded. With further reduction in signal availability the number of available pseudorange measurements reduced to fewer than four conventional calculation of the navigation solution via trilateration with four unknowns is not possible. In contrast to state-of-the-art approaches addressing NLOS signal reception at the pseudorange measurement level, we directly address and constructively use NLOS signals at the signal level via DPE using NLOS signals. OUR APPROACH: DPE USING NLOS SIGNALS We first model NLOS signals as LOS signals to virtual satellites at satellite mirror-image positions, as shown in FIGURE 1. This approach is similar to using virtual transmitters for multipath-assisted wireless indoor positioning. We calculate these satellite mirror-image positions and velocities using knowledge of building reflection surfaces estimated from available 3D maps. We then integrate these NLOS signals into positioning via DPE. We modify the expected signal reception Satellite mirror-image Position residual East used in DPE to include NLOS signal information, as shown in FIGURE 2. Our approach deeply integrates this information and accurately describes the actual received signal. In addition, our approach provides a navigation solution under reduced signal availability. FIGURE 3 shows a block diagram of our approach. IMPLEMENTATION AND EXPERIMENT RESULTS We implemented DPE using NLOS signals with commercial frontend components and our software platform, PyGNSS. We conducted an experiment in front of the 53 meters Navigation estimate Position residual North Overall correlation FIGURE 2 Overall correlation in DPE, with the NLOS signal treated as an additional LOS signal to a virtual satellite at the satellite mirror-image position. by 40 meters wind tunnel located at NASA s Ames Research Center, Mountain View, California (see FIGURE 4). The material of the vertical surface of the wind tunnel s air-intake port is a metal wire mesh with a grid spacing of 1.8 centimeters by 1.8 centimeters, as shown in FIGURE 5. This grid spacing is approximately one tenth of the carrier wavelength of the L1 signal; the mesh wire radius is much less than the grid spacing. Thus, the vertical surface of the air-intake port acts as a reflector of L1 signals. We estimated the normal vector and a point on the wind tunnel s reflection surface using a geo- 44 WORLD MARCH 2017

4 Initialize receiver candidates (a) Building reflection surface Calculate satellite PVTs Calculate satellite mirror-images Determine possible LOS & NLOS paths Generate LOS & NLOS signal replicas Batch correlation using FFT (b) Non-coherent accumulation Estimate PVT from overall correlation FIGURE 3 Block diagram of DPE using NLOS signals and involving calculation of satellite position, velocity and time (PVT) and batch correlation using a fast Fourier transform (FFT). referenced 3D point cloud available on line through the National Oceanic and Atmospheric Administration s (NOAA s) Data Access Viewer tool ( lidar). We refined the estimate using iterative closest point map-matching with a lidar scan (FIGURE 6). We then determined possible LOS and NLOS paths from satellite elevation-azimuth plots. Plotted in FIGURE 7 are the satellite positions, the satellite mirror-image positions and the building reflection surface. An NLOS path to a satellite exists if the corresponding LOS path to the satellite mirror-image intersects the building reflection surface. In our experiment, LOS paths exist to satellite PRNs 5, 7, 27 and 28 and an NLOS path exists to satellite PRN 5. Thus, both LOS and NLOS signals from satellite PRN 5 are present. This is verified by examining the amplitude of the in-phase prompt correlations over time. Only the inphase prompt correlations of satellite PRN 5 exhibit a sinusoidal behavior characteristic of having both LOS and NLOS signals, as shown in FIGURE 8. We then performed DPE, including the signal correlation contribution from the NLOS path to satellite PRN 5, where the NLOS path is represented as a LOS path to the satellite mirrorimage. The overall correlation result, including the signal correlation from the NLOS path to satellite PRN 5, is shown in FIGURE 9. The color of the position markers, plotted using Google Maps, represents the overall correlation amplitude. Red indicates a high overall correlation amplitude and blue indicates a low overall correlation amplitude. The navigation solution is directly estimated as a correlation-weighted mean of the navigation candidates. The result, as compared to that estimated using pseudoranges from scalar tracking followed by trilateration, is shown in FIGURE 10. DPE using NLOS signals demonstrated improved horizontal positioning accuracy by 40 meters. FIGURE 4 Experiment setup in front of the 53 meters by 40 meters wind tunnel located at NASA s Ames Research Center, Mountain View, California. (a) data collection equipment; (b) wide-angle photograph of the wind tunnel s air-intake port. CONCLUSION In summary, we proposed DPE using NLOS signals to mitigate the issues of NLOS signal reception and reduced signal availability in urban navigation. We modeled NLOS signals as LOS signals to virtual satellites at satellite mirrorimage positions. In this manner, NLOS signals are transformed from being unwanted interference to becoming additional useful navigation signals. We then created expected signal receptions to include NLOS signal information at multiple potential navigation candidates and use DPE for positioning. Finally, we experimentally demonstrated a reduction in horizontal positioning MARCH WORLD 45

5 (a) (b) 1.8 cm FIGURE 5 Metal wire mesh on the vertical surface of the wind tunnel s air-intake port. (a) close-up photograph showing the grid spacing of 1.8 centimeters by 1.8 centimeters; (b) photograph from another perspective showing wire mesh covering the entire vertical surface of the air-intake port. error by 40 meters. This is in comparison to the navigation result obtained using pseudoranges estimated from conventional scalar tracking followed by trilateration. ACKNOWLEDGMENTS The authors thank the Safe Autonomous Flight Environment (SAFE50) and the Unmanned Aircraft System Traffic Management teams at NASA s Ames Research Center, where the lead author was hosted for the summer of 2016, for their equipment support. The authors also thank Akshay Shetty for collecting and map-matching the lidar scan to the geo-referenced 3D point cloud. This article is based on the paper Direct Position Estimation Utilizing Non-Line-of-Sight (NLOS) Signals presented at ION GNSS+ 2016, the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation, held Sept , 2016, in Portland, Oregon. YUTING NG received her B.S. degree in electrical engineering and her M.S. degree in aerospace engineering from the University of Illinois at Urbana-Champaign (UIUC) in 2014 and 2016, respectively. Her research interests are advanced signal processing, satellite navigation systems and radar. GRACE XINGXIN GAO is an assistant professor in the Aerospace Engineering Department at UIUC. She obtained her Ph.D. degree in electrical engineering from the Laboratory at Stanford University in 8. Before joining UIUC in 2012, she was a research associate at Stanford University. Missing building reflection surface Refined building not geo-referenced reflection surface 3D map from NOAA DAV Lidar scan FIGURE 6 Building reflection surface estimated from NOAA Data Access Viewer (DAV) point cloud, refined using map-matching with a lidar scan. 46 WORLD MARCH 2017

6 FIGURE 7 Elevation-azimuth plot with satellites highlighted using green boxes and satellite mirror-images highlighted using red boxes. The 3D point cloud of the wind tunnel s air-intake port is plotted using grey dots. The path to the mirror-image of satellite PRN 5 passes through the surface of the wind tunnel. Thus, an NLOS path to satellite PRN 5 exists. In addition, LOS paths exist to satellite PRNs 5, 7, 27 and PRN 5 PRN PRN 7 PRN 28 FIGURE 8 Only the in-phase prompt correlation of satellite PRN 5 exhibits a sinusoidal behavior characteristic of having both LOS and NLOS signal components. 1.0 Normalized correlation 0.9 amplitude Estimated result Position of receiver Position of receiver m 45 m Estimated result Our approach DPE utilizing NLOS signals Conventional approach Scalar tracking + trilateration FIGURE 9 Normalized overall correlation with contributions from all satellites, including the satellite mirror-image of PRN FIGURE 10 DPE using NLOS signals demonstrates improved horizontal positioning accuracy by 40 meters. This is in comparison to the navigation result obtained using pseudoranges estimated from conventional scalar tracking followed by trilateration. Further Reading Authors Conference Paper Direct Position Estimation Utilizing Non- Line-of-Sight (NLOS) Signals by Y. Ng and G.X. Gao in Proceedings of ION GNSS+ 2016, the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept , 2016, pp Non-Line-of-Sight Signals GNSS Solutions: Multipath vs. NLOS Signals: How Does Non-Line-of-Sight Reception Differ from Multipath Interference by M. Petovello with P. Groves in Inside GNSS, Vol. 8, No. 6, Nov./Dec. 2013, pp Available on line: Direct Position Estimation Mitigating Jamming and Meaconing Attacks Using Direct Positioning by Y. Ng and G.X. Gao in Proceedings of IEEE/ ION PLANS 2016, the Position, Location, and Navigation Symposium, Savannah, Georgia, April 11 14, 2016, pp , doi: / PLANS Evaluation of GNSS Direct Position Estimation in Realistic Multipath Channels by P. Closas, C. Fernández-Prades, J. Fernández-Rubio, M. Wis, G. Vecchione, F. Zanier, J.A. Garcia-Molina and M. Crisci in Proceedings of ION GNSS+ 2015, the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation, Tampa, Florida, Sept , 2015, pp Collective Detection: Enhancing GNSS Receiver Sensitivity by Combining Signals from Multiple Satellites by P. Axelrad, J. Donna, M. Mitchell and S. Mohiuddin in World, Vol. 21, No. 1, Jan. 2010, pp Available on line: Resources/gpsworld.january10.pdf On the Maximum Likelihood Estimation of Position by P. Closas, C. Fernández-Prades and J. Fernández-Rubio in Proceedings of ION GNSS 6, the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, Sept , 6, pp PyGNSS Python GNSS Receiver: An Object-Oriented Software Platform Suitable for Multiple Receivers by E. Wycoff, Y. Ng and G.X. Gao in World, Vol. 26, No. 2, Feb. 2015, pp Available on line: innovation-python-gnss-receiver/ 3D Maps for Multipath Detection NLOS Correction/Exclusion for GNSS Measurement Using RAIM and City Building Models by L.-T. Hsu, Y. Gu and S. Kamijo in Sensors, Vol. 15, No. 7, 2015, pp , doi: /s Multipath Detection and Rectification Using 3D Maps by S. Miura, S. Hisaka and S. Kamijo in Proceedings of ITSC 2013, the16th International IEEE Conference on Intelligent Transportation Systems, The Hague, The Netherlands, Oct. 6 9, 2013, pp , doi: /ITSC Urban Multipath Detection and Mitigation with Dynamic 3D Maps for Reliable Land Vehicle Localization by M. Obst, S. Bauer and G. Wanielik in Proceedings of IEEE/ION PLANS 2012, the Position, Location, and Navigation Symposium, Myrtle Beach, South Carolina, April 23 26, 2012, pp , doi: / PLANS Virtual Transmitters Simultaneous Localization and Mapping in Multipath Environments by C. Gentner, B. Ma, M. Ulmschneider, T. Jost and A. Dammann in Proceedings of IEEE/ION PLANS 2016, the Position, Location, and Navigation Symposium, Savannah, Georgia, April 11 14, 2016, pp , doi: /PLANS MARCH WORLD 47