Detecting, estimating and correcting multipath biases affecting GNSS signals using a marginalized likelihood ratio-based methodReport as inadecuate




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1 Northwestern Polytechnical University 2 DEOS - Département Electronique, Optronique et Signal 3 IRIT - Institut de recherche en informatique de Toulouse

Abstract : In urban canyons, non-line-of-sight NLOS multipath interferences affect position estimation based on global navigation satellite systems GNSS. This paper proposes to model the effects of NLOS multipath interferences as mean value jumps contaminating the GNSS pseudo-range measurements. The marginalized likelihood ratio test MLRT is then investigated to detect, identify and estimate the corresponding NLOS multipath biases. However, the MLRT test statistics is difficult to compute. In this work, we consider a Monte Carlo integration technique based on bias magnitude sampling. Jensen-s inequal- ity allows this Monte Carlo integration to be simplified. The multiple model algorithm is also used to update the prior information for each bias magnitude sample. Some strategies are designed for estimating and correcting the NLOS multipath biases. In order to demonstrate the performance of the MLRT, experiments allowing several localization methods to be compared are performed. Finally, results from a measurement campaign conducted in an urban canyon are presented in order to evaluate the performance of the proposed algorithm in a representative environment.

Keywords : Global navigation satellite systems Multipath mitigation Marginalized likelihood ratio test Multiple model Urban positioning





Author: Cheng Cheng - Jean-Yves Tourneret - Quan Pan - Vincent Calmettes -

Source: https://hal.archives-ouvertes.fr/



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