基于粒子滤波和似然比的接收机自主完好性监测算法

由于粒子滤波算法在处理非线性系统非高斯噪声问题具有较大的优势,提出将粒子滤波算法与对数似然比方法有机结合应用于接收机自主完好性监测(Receiver autonomous integrity monitoring,RAIM)中。通过粒子滤波算法对状态进行精确估计,利用对数似然比建立一致性检验统计量进行故障检测。在建立全量累加对数似然比和部分累加对数似然比检验统计值的基础上,通过比较系统各状态累加对数似然比和检测阈值之间的关系,进而对卫星故障进行检测。对算法进行了数学建模,描述了RAIM算法流程。通过实测数据对提出的RAIM算法进行验证,结果表明:粒子滤波在非高斯测量噪声情况下可以对GPS接收机状态进行精确的估计,利用对数似然比建立的一致性检验统计量能有效地检测并隔离故障卫星,验证了该算法应用于接收机自主完好性监测的可行性和有效性。

RAIM Algorithm Based on Particle Filter and Likelihood Ratio Method

Particle filter algorithm has a great advantage in processing nonlinear and non-Gaussian noise problems, so an approach combining the particle filter algorithm with the log-likelihood ratio (LLR) is presented for GPS receiver autonomous integrity monitoring (RAIM). The state estimate is calculated precisely by the particle filter algorithm and LLR is used as a consistency test statistic to achieve the fault detection. By setting up the total and partial cumulative LLRs, the satellite fault is detected by checking the cumulative LLR of system state with detection threshold. The mathematical model of the algorithm is established. Meanwhile, the algorithm flow is described. Based on the real GPS data, the RAIM algorithm is tested. Experimental result demonstrates that the particle filter algorithm can accurately estimate the state of GPS receiver under conditions of non-Gaussian measurement noise, and LLR as the statistic of consistency test can effectively detect and isolate fault satellite, thus validating the feasibility and validity of particle filter and likelihood ratio methods for RAIM.