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A Computation Effective Range-Based 3D Mapping Aided GNSS with NLOS Correction Method

Published online by Cambridge University Press:  30 June 2020

Hoi-Fung Ng ,
Guohao Zhang Open the ORCID record for Guohao Zhang [Opens in a new window]  and
Li-Ta Hsu Open the ORCID record for Li-Ta Hsu [Opens in a new window]
Hoi-Fung Ng
Affiliation:
(Interdisciplinary Division of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University)
Guohao Zhang
Affiliation:
(Interdisciplinary Division of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University)
Li-Ta Hsu*
Affiliation:
(Interdisciplinary Division of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University)
*
(E-mail: [email protected])
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Abstract

Global navigation satellite system (GNSS) positioning in dense urban areas remains a challenge due to the signal reflection by buildings, namely multipath and non-line-of-sight (NLOS) reception. These effects degrade the performance of low-cost GNSS receivers such as in those smartphones. An effective three-dimensional (3D) mapping aided GNSS positioning method is proposed to correct the NLOS error. Instead of applying ray-tracing simulation, the signal reflection points are detected based on a skyplot with the surrounding building boundaries. The measurements of the direct and reflected signals can thus be simulated and further used to determine the user's position based on the measurement likelihood between real measurements. Verified with real experiments, the proposed algorithm is able to reduce the computational load greatly while maintaining a positioning accuracy within 10 metres of error in dense urban environments, compared with the conventional method of ray-tracing based NLOS corrected positioning.

Keywords

3DMA GNSSNLOSUrban Canyon3D Building Model
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 6 , November 2020 , pp. 1202 - 1222
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Copyright
Copyright © The Royal Institute of Navigation 2020

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References

REFERENCES

Adjrad, M. and Groves, P. D. (2018a). Intelligent urban positioning: integration of shadow matching with 3D-mapping-aided GNSS ranging. Journal of Navigation, 71(1), 1–20.CrossRefGoogle Scholar
Adjrad, M. and Groves, P. D. (2018b) Real-Time 3D Mapping Aided GNSS on Android Devices. Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+2018). Miami, Florida, Institute of Navigation (ION), 345–356.Google Scholar
Adjrad, M., Groves, P. D., Quick, J. C. and Ellul, C. (2019). Performance assessment of 3D-mapping-aided GNSS part 2: Environment and mapping. Navigation, 66(2), 363–383.CrossRefGoogle Scholar
Biljecki, F., Ledoux, H., Stoter, J. and Zhao, J. (2014). Formalisation of the level of detail in 3D city modelling. Computers, Environment and Urban Systems, 48, 1–15.CrossRefGoogle Scholar
Dabove, P. and Petovello, M. G. (2014). What are the actual performances of GNSS positioning using smartphone technology? Inside GNSS, 9, 34–37.Google Scholar
Gao, H. and Groves, P. D. (2018). Environmental context detection for adaptive navigation using GNSS measurements from a smartphone. Navigation, 65(1), 99–116.CrossRefGoogle Scholar
Groves, P. D. (2011). Shadow matching: a new GNSS positioning technique for urban canyons. Journal of Navigation, 64(3), 417–430.CrossRefGoogle Scholar
Groves, P. (2013). Multipath vs. NLOS signals. Inside GNSS, 8, 40–42.Google Scholar
Groves, P. D. and Adjrad, M. (2017). Likelihood-based GNSS positioning using LOS/NLOS predictions from 3D mapping and pseudoranges. GPS Solutions, 21(4), 1805–1816.CrossRefGoogle Scholar
Groves, P. D. and Adjrad, M. (2019). Performance assessment of 3D-mapping–aided GNSS part 1: algorithms, user equipment, and review. Navigation, 66(2), 341–362.CrossRefGoogle Scholar
Groves, P. D. and Jiang, Z. (2013). Height aiding, C/N0 weighting and consistency checking for GNSS NLOS and multipath mitigation in urban areas. Journal of Navigation, 66(5), 653–669.CrossRefGoogle Scholar
Gu, Y. and Kamijo, S. (2017). GNSS positioning in deep urban city with 3D map and double reflection. 2017 European Navigation Conference (ENC), Lausanne, 84–90.CrossRefGoogle Scholar
Gu, Y., Hsu, L. and Kamijo, S. (2016). GNSS/onboard inertial sensor integration with the aid of 3-D building map for lane-level vehicle self-localization in urban canyon. IEEE Transactions on Vehicular Technology, 65(6), 4274–4287.CrossRefGoogle Scholar
Hsu, L.-T. (2018). Analysis and modeling GPS NLOS effect in highly urbanized area. GPS Solutions, 22 (1), 7.CrossRefGoogle Scholar
Hsu, L.-T. and Kamijo, S. (2015). NLOS Exclusion Using Consistency Check and City Building Model in Deep Urban Canyons. Proceedings of the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+2015), Tampa, Florida, 2390–2396.Google Scholar
Hsu, L.-T., Gu, Y. and Kamijo, S. (2015). NLOS correction/exclusion for GNSS measurement using RAIM and city building models. Sensors (Basel, Switzerland), 15(7), 17329–17349.CrossRefGoogle ScholarPubMed
Hsu, L.-T., Hu, Y. and Kamijo, S. (2016a). 3D building model-based pedestrian positioning method using GPS/GLONASS/QZSS and its reliability calculation. GPS Solutions, 20(3), 413–428.CrossRefGoogle Scholar
Hsu, L., Gu, Y., Huang, Y. and Kamijo, S. (2016b). Urban pedestrian navigation using smartphone-based dead reckoning and 3-D map-aided GNSS. IEEE Sensors Journal, 16(5), 1281–1293.CrossRefGoogle Scholar
Hsu, L., Tokura, H., Kubo, N., Gu, Y. and Kamijo, S. (2017). Multiple faulty GNSS measurement exclusion based on consistency check in urban canyons. IEEE Sensors Journal, 17(6), 1909–1917.CrossRefGoogle Scholar
Klobuchar, J. A. (1987). Ionospheric time-delay algorithm for single-frequency GPS users. IEEE Transactions on Aerospace and Electronic Systems AES, 23(3), 325–331.CrossRefGoogle Scholar
Miura, S., Hsu, L.-T. and Chen, F. (2015). GPS error correction with pseudorange evaluation using three-dimensional maps. IEEE Transactions on Intelligent Transportation Systems, 16(6), 3104–3115.CrossRefGoogle Scholar
Moreau, J., Ambellouis, S. and Ruichek, Y. (2017). Fisheye-based method for GPS localization improvement in unknown semi-obstructed areas. Sensors, 17(1), 119.CrossRefGoogle ScholarPubMed
Ng, H.-F., Zhang, G. and Hsu, L.-T. (2019). Range-Based 3D Mapping Aided GNSS with NLOS Correction Based on Skyplot with Building Boundaries. ION Pacific PNT 2019. Manuscript submitted.CrossRefGoogle Scholar
Obst, M., Bauer, S. and Wanielik, G. (2012). Urban Multipath Detection and Mitigation With Dynamic 3D Maps for Reliable Land Vehicle Localization. Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium. 685–691.CrossRefGoogle Scholar
Pesyna, K. M., Heath, R. W. and Humphreys, T. E. (2014). Centimeter Positioning With a Smartphone-Quality GNSS Antenna. Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+2014). Tampa, Florida, 1568–1577.Google Scholar
Realini, E. and Reguzzoni, M. (2013). GoGPS: open source software for enhancing the accuracy of low-cost receivers by single-frequency relative kinematic positioning. Measurement Science and Technology, 24(11), 115010.CrossRefGoogle Scholar
Sun, R., Hsu, L.-T., Xue, D., Zhang, G. and Ochieng, W. Y. (2019). GPS signal reception classification using adaptive neuro-fuzzy inference system. Journal of Navigation, 72(3), 685–701.CrossRefGoogle Scholar
Survey and Mapping Office (1995). Explanatory Notes on Geodetic Datums in Hong Kong (minor revision, 2018) [document]. HKSAR: Lands Department.Google Scholar
Suzuki, T. and Kubo, N. (2013). Correcting GNSS Multipath Errors Using a 3D Surface Model and Particle Filter. Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+2013), 1583–1595.Google Scholar
Suzuki, T. and Kubo, N. (2014). N-LOS GNSS Signal Detection Using Fish-Eye Camera for Vehicle Navigation in Urban Environments. 27th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2014, Vol. 3: 1897–1906.Google Scholar
Wang, L., Groves, P. D. and Ziebart, M. K. (2012). Multi-constellation GNSS performance evaluation for urban canyons using large virtual reality city models. Journal of Navigation, 65(3), 459–476.CrossRefGoogle Scholar
Wang, L., Groves, P. D. and Ziebart, M. K. (2013). GNSS shadow matching: improving urban positioning accuracy using a 3D city model with optimized visibility scoring scheme. Navigation, 65, 195–207.CrossRefGoogle Scholar
Wen, W., Zhang, G. and Hsu, L.-T. (2018a). Correcting GNSS NLOS by 3D LiDAR and Building Height. Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+2018), 3156–3168.CrossRefGoogle Scholar
Wen, W., Zhang, G. and Hsu, L. (2018b). Exclusion of GNSS NLOS Receptions Caused by Dynamic Objects in Heavy Traffic Urban Scenarios Using Real-Time 3D Point Cloud: An Approach Without 3D Maps. 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), 158–165.Google Scholar
Xu, B., Jia, Q., Luo, Y. and Hsu, L. T. (2019). Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX-and NMEA-Level Measurements. Remote Sensing, 11(16), 1851.CrossRefGoogle Scholar
Ziedan, N. I. (2017). Urban Positioning Accuracy Enhancement Utilizing 3D Buildings Model and Accelerated Ray Tracing Algorithm. Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+2017), 3253–3268.CrossRefGoogle Scholar