A maximum likelihood-based unscented Kalman filter for multipath mitigation in a multi-correlator based GNSS receiver

Cheng Cheng 1 Quan Pan 1 Vincent Calmettes 2 Jean-Yves Tourneret 3, 4
1 NPU - Northwestern Polytechnical University [Xi'an]
2 DEOS - Département Electronique, Optronique et Signal
3 IRIT - Institut de recherche en informatique de Toulouse
4 TéSA - Telecommunications for Space ant Aeronautics
Abstract : In complex environments, the presence or absence of multipath signals not only depends on the relative motion between the GNSS receiver and navigation satellites, but also on the environment where the receiver is located. Thus it is difficult to use a specific propagation model to accurately capture the dynamics of multipath signal parameters when the GNSS receiver is moving in urban canyons or other severe obstructions. This paper introduces a statistical model for the line-of-sight and multipath signals received by a GNSS receiver. A multi-correlator based GNSS receiver is also exploited with the advantage to fully characterizing the impact of multipath signals on the correlation function by providing samples of the whole correlation function. Finally, a maximum likelihood-based unscented Kalman filter is investigated to estimate the line-of-sight and multipath signal parameters. Numerical simulations clearly validate the effectiveness of the proposed approach.
Keywords : Un-scented Kalman filter Global navigation satellite systems Multipath mitigation Multi-correlator Maximum likelihood principle
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016), Mar 2016, Shanghai, China. Proceedings of ICASSP 2016, pp. 6560-6564, 2016
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Soumis le : jeudi 16 mars 2017 - 15:08:42
Dernière modification le : mercredi 22 mars 2017 - 01:08:10

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  • OATAO : 17098

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Cheng Cheng, Quan Pan, Vincent Calmettes, Jean-Yves Tourneret. A maximum likelihood-based unscented Kalman filter for multipath mitigation in a multi-correlator based GNSS receiver. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016), Mar 2016, Shanghai, China. Proceedings of ICASSP 2016, pp. 6560-6564, 2016. <hal-01491205>

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