GNSS positioning in deep urban city with 3D map and double reflection

Accurate 3D city map becomes available and commercialized for various applications. 3D map information could improve the performance of GNSS positioning in the city urban environment by recognizing the Non-Line-Of-Sight and multipath signal. In ENC 2015, we have presented the idea of the position hypothesis based GNSS positioning method using 3D maps, called 3D-GNSS. After that, we further improved the 3D-GNSS with considering the differential correction, which has been published in ENC 2016. In our previous works, 3D-GNSS corrects the pseudorange delay by recognizing the single reflection path of satellite signals using ray-tracing and 3D map information. However, the double reflection, which means that the signal is reflected twice from satellite to receiver, is quite possible to be existing, especially in deep urban area with many skyscrapers. Thus, this paper proposes to improve the positioning performance by considering both single and double reflections in our 3D-GNSS method. The result shows that the positioning error could be reduced by 0.8 meter with the proposed idea.

[1]  Jun-ichi Meguro,et al.  GPS Multipath Mitigation for Urban Area Using Omnidirectional Infrared Camera , 2009, IEEE Transactions on Intelligent Transportation Systems.

[2]  Michael S. Braasch,et al.  Performance comparison of multipath mitigating receiver architectures , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[3]  William Whittaker,et al.  A robust approach to high‐speed navigation for unrehearsed desert terrain , 2007 .

[4]  Li-Ta Hsu,et al.  3D building model-based pedestrian positioning method using GPS/GLONASS/QZSS and its reliability calculation , 2016, GPS Solutions.

[5]  Li-Ta Hsu,et al.  Urban Pedestrian Navigation Using Smartphone-Based Dead Reckoning and 3-D Map-Aided GNSS , 2016, IEEE Sensors Journal.

[6]  Li-Ta Hsu,et al.  GNSS/Onboard Inertial Sensor Integration With the Aid of 3-D Building Map for Lane-Level Vehicle Self-Localization in Urban Canyon , 2016, IEEE Transactions on Vehicular Technology.

[7]  Yassine Ruichek,et al.  Fisheye-Based Method for GPS Localization Improvement in Unknown Semi-Obstructed Areas , 2017, Sensors.

[8]  Lei Wang,et al.  GNSS Shadow Matching: Improving Urban Positioning Accuracy Using a 3D City Model with Optimized Visibility Scoring Scheme , 2013 .

[9]  P. Groves Shadow Matching: A New GNSS Positioning Technique for Urban Canyons , 2011, Journal of Navigation.

[10]  Marcus Obst,et al.  Multipath detection with 3D digital maps for robust multi-constellation GNSS/INS vehicle localization in urban areas , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[11]  Li-Ta Hsu,et al.  Passive Sensor Integration for Vehicle Self-Localization in Urban Traffic Environment † , 2015, Sensors.

[12]  P. Groves,et al.  Smartphone Shadow Matching for Better Cross-street GNSS Positioning in Urban Environments , 2015 .

[13]  Juliette Marais,et al.  Toward accurate localization in guided transport: combining GNSS data and imaging information , 2014 .

[14]  L-T. Hsu Integration of Vector Tracking Loop and Multipath Mitigation Technique and its Assessment , 2013 .

[15]  S. Kamijo,et al.  A STUDY OF CITY BUILDING MODEL BASED POSITIONING METHOD USING MULTI-GNSS IN DEEP URBAN CANYON , 2015 .

[16]  S. Kamijo,et al.  Autonomous driving positioning using building model and DGNSS , 2016, 2016 European Navigation Conference (ENC).

[17]  Agus Budiyono,et al.  Principles of GNSS, Inertial, and Multi-sensor Integrated Navigation Systems , 2012 .

[18]  Paul D. Groves GNSS Solutions: Multipath vs. NLOS signals. How Does Non-Line-of-Sight Reception Differ From Multipath Interference , 2013 .

[19]  Takashi Suzuki,et al.  An Effective Method for Multipath Mitigation under Severe Multipath Environments , 2005 .

[20]  Marcus Obst,et al.  Evaluation of Shadow Maps for Non-Line-of-Sight Detection in Urban GNSS Vehicle Localization with VANETs - The GAIN Approach , 2013, 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).