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An EM-based multipath interference mitigation in GNSS receivers

delete2019-09-01
delete14
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OA
AI
C
Cheng Cheng *
J
Jean‐Yves Tourneret
DOI:10.1016/j.sigpro.2019.04.018delete
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摘要

摘要

En 中文
In multipath (MP) environments, the received signals depend on several factors related to the global navigation satellite systems (GNSS) receiver environment and motion. Thus it is difficult to use a specific propagation model to accurately capture the dynamics of the MP signal when the GNSS receiver is moving in urban canyons. This paper formulates the problem of MP interference mitigation in the GNSS receiver as a joint state (containing the direct signal parameters) and time-varying model parameter (containing the MP signal parameters) estimation. Accordingly, we propose to exploit the EM algorithm for achieving the joint state and time-varying parameter estimation in the context of MP interference mitigation in GNSS receivers. More precisely, the proposed EM-based MP mitigation approach is decomposed into two iterative steps: (a) the posterior pdf of the direct signal parameters and the expected log-likelihood function necessary in the expectation step of the EM algorithm are approximated by using an appropriate particle filter; (b) the maximum likelihood solution for MP signal parameters is then obtained using Newton's method in the maximization step. The convergence of the proposed approach is analyzed based on the existing convergence theorem associated with the EM algorithm. Finally, a comprehensive simulation study is conducted to compare the performance of the proposed EM-based MP mitigation approach with other state-of-the-art MP mitigation approaches in static and realistic scenarios. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Global navigation satellite systems
Multipath mitigation
Expectation-maximization
Particle filter
Newton iteration
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期刊

Signal Processing 封面图
Signal Processing
IF:
3.6
论文数:
10.0K
被引数:
1.7W

机构

U
universite de toulouse
学者数:
3.5W
论文数: 2.7W
被引数: 37
N
Northwestern Polytechnical University
学者数:
4.6W
论文数: 3.7W
被引数: 5.3W
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