Journal
ADVANCES IN SPACE RESEARCH
Volume 61, Issue 5, Pages 1260-1272Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.asr.2017.12.016
Keywords
Global positioning system; RAIM; Particle swarm optimization; Particle filter; Chaos theory
Categories
Funding
- National Natural Science Foundation of China [61571309, 61101161]
- Liaoning BaiQianWan Talents Program
- Scientific Study Project for Liaoning Province Ministry of Education [L201716]
- Aerospace Science Foundation of Avic [2015ZC54010]
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The receiver autonomous integrity monitoring (RAIM) is one of the most important parts in an avionic navigation system. Two problems need to be addressed to improve this system, namely, the degeneracy phenomenon and lack of samples for the standard particle filter (PF). However, the number of samples cannot adequately express the real distribution of the probability density function (i.e., sample impoverishment). This study presents a GPS receiver autonomous integrity monitoring (RAIM) method based on a chaos particle swarm optimization particle filter (CPSO-PF) algorithm with a log likelihood ratio. The chaos sequence generates a set of chaotic variables, which are mapped to the interval of optimization variables to improve particle quality. This chaos perturbation overcomes the potential for the search to become trapped in a local optimum in the particle swarm optimization (PSO) algorithm. Test statistics are configured based on a likelihood ratio, and satellite fault detection is then conducted by checking the consistency between the state estimate of the main PF and those of the auxiliary PFs. Based on GPS data, the experimental results demonstrate that the proposed algorithm can effectively detect and isolate satellite faults under conditions of non-Gaussian measurement noise. Moreover, the performance of the proposed novel method is better than that of RAIM based on the PF or PSO-PF algorithm. (C) 2017 COSPAR. Published by Elsevier Ltd. All rights reserved.
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