Stochastic GNSS Multipath Estimation Using a Particle Filter

Andreas Tollkühn, Fabian Amtmann, Florian Henkenhaf, Florian Mickler, Lucila Patino-Studencki, Jörn Thielecke

Peer Reviewed

Abstract: Future automotive applications rely on precise positioning systems at a very high confidence level. Under open sky conditions latest low-cost receivers already achieve lane level accuracy. However, this accuracy cannot be warranted in urban areas. We present a realistic GNSS multipath model for pseudorange measurements. We derive an adaptive non-Gaussian model from a Laplacian multipath model; the obtained probability distribution denoted by K0 is given by its pdf that follows a modified Bessel function of the second kind K0. It describes GNSS pseudorange measurements in multipath environment better than a normal distribution. Based on that multipath model we have developed a bootstrap particle filter. It includes an adaptive measurement model with a multipath detection algorithm, which switches between multipath-free Gaussian distributed errors and multipath-affected K0-distributed ones. The algorithms are tested by using both simulation and real-world data. We demonstrate that the proposed model and algorithm reduce the position error by more than 20% in the simulation and by 5% using real measurements and give more suitable confidence information.
Published in: Proceedings of the 2016 International Technical Meeting of The Institute of Navigation
January 25 - 28, 2016
Hyatt Regency Monterey
Monterey, California
Pages: 858 - 864
Cite this article: Tollkühn, Andreas, Amtmann, Fabian, Henkenhaf, Florian, Mickler, Florian, Patino-Studencki, Lucila, Thielecke, Jörn, "Stochastic GNSS Multipath Estimation Using a Particle Filter," Proceedings of the 2016 International Technical Meeting of The Institute of Navigation, Monterey, California, January 2016, pp. 858-864.
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