文章基本信息

标题：Research on Particle Filter Based on Neural Network for Receiver Autonomous Integrity Monitoring
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作者：Ershen Wang ; Qing Zhang ; Tao Pang 等
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2016
卷号：14
期号：1
页码：203-210
DOI：10.12928/telkomnika.v14i1.2359
语种：English
出版社：Universitas Ahmad Dahlan
摘要：According to the measurement noise feature of GPS receiver and the degeneracy phenomenon of particle filter (PF), in order to alleviate the sample impoverishment problem for PF, GPS receiver autonomous integrity monitoring (RAIM) algorithm based on PF algorithm combining neural network was proposed, which was used to improve the importance state adjustment of particle filter algorithm. The PF algorithm based on neural network is analized. And the test statistic of satellite fault detection is set up. The satellite fault detection is undertaken by checking the cumulative log-likelihood ratio (LLR) of system state of GPS receiver.The proposed algorithm was Validated by the measured real raw data from GPS receiver, which are deliberately contaminated with the bias fault and ramp fault, the simulation results demonstrate that the proposed algorithm can accurately estimate the state of GPS receiver in the case of non-Gaussian measurement noise, effectively detect and isolate fault satellite by establishing log-likelihood ratio statistic for consistency test and improve the accuracy of detection performance.
其他摘要：According to the measurement noise feature of GPS receiver and the degeneracy phenomenon of particle filter (PF), in order to alleviate the sample impoverishment problem for PF, GPS receiver autonomous integrity monitoring (RAIM) algorithm based on PF algorithm combining neural network was proposed, which was used to improve the importance state adjustment of particle filter algorithm. The PF algorithm based on neural network is analized. And the test statistic of satellite fault detection is set up. The satellite fault detection is undertaken by checking the cumulative log-likelihood ratio (LLR) of system state of GPS receiver.The proposed algorithm was Validated by the measured real raw data from GPS receiver, which are deliberately contaminated with the bias fault and ramp fault, the simulation results demonstrate that the proposed algorithm can accurately estimate the state of GPS receiver in the case of non-Gaussian measurement noise, effectively detect and isolate fault satellite by establishing log-likelihood ratio statistic for consistency test and improve the accuracy of detection performance.
关键词：Global Positioning System (GPS);Receiver Autonomous Integrity Monitoring (RAIM);Particle Filter (PF);Particle Degeneracy;General Regression Neural Network (GRNN)