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Shadow Matching: Improving Smartphone GNSS Positioning in Urban Environments

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 245)

Abstract

Positioning using Global Navigation Satellite Systems (GNSS) is unreliable in dense urban areas. The accuracy in the across-street direction can degrade to a few tens of meters in deep urban canyons because the unobstructed satellite signals travel along the street, rather than across it, resulting in poor signal geometry. A new solution to this cross-street positioning problem is to use 3D city models to predict satellite visibility, and then compare with the measured satellite visibility to determine position. This concept is known as shadow matching. In this work, for the first time, the shadow-matching technique is demonstrated using GNSS data from a smartphone. The algorithm has been optimized for speed. The system is then verified with real-world GPS and GLONASS data from Samsung Galaxy S3 smartphones. The experimental data show that shadow matching outperforms the conventional GNSS positioning, improving cross-street positioning success rate for a 2 m accuracy from 18.5 to 86.9 % of the time at the selected test site. The system is also compatible with Beidou and Galileo, with potentially improved performance.

Keywords

  • GNSS
  • Urban canyons
  • 3D city model
  • Shadow matching
  • Smartphone

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Acknowledgments

The authors gratefully acknowledge Mr. Kimon Voutsis for his support with the experiments and Dr. Ziyi Jiang for his support on design of experiments. This work has been jointly funded by the University College London Engineering Faculty Scholarship Scheme and the Chinese Scholarship Council.

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Correspondence to Lei Wang .

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Wang, L., Groves, P.D., Ziebart, M.K. (2013). Shadow Matching: Improving Smartphone GNSS Positioning in Urban Environments. In: Sun, J., Jiao, W., Wu, H., Shi, C. (eds) China Satellite Navigation Conference (CSNC) 2013 Proceedings. Lecture Notes in Electrical Engineering, vol 245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37407-4_57

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  • DOI: https://doi.org/10.1007/978-3-642-37407-4_57

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