Research on GPS RAIM Algorithm Based on SIR Particle Filtering State Estimation and Smoothed Residual

Abstract

The investigation presents a new approach based on SIR particle filtering state estimation and smoothed residual for GPS receiver autonomous integrity monitoring (RAIM), which adopted the difference value between the ideal observation values acquired by state estimation and the actual state observation values, and the log likelihood ratio (LLR) test based on probability density function of state-measurement was set up. Experimental results based on real GNSS data demonstrate that the algorithm can estimate the state precisely under non-Gaussian measurement noise, detect and isolate GPS satellite failures successfully and solve the performance degradation problem of RAIM algorithm based on Kalman filter. Therefore, experimental results validate the validity of SIR particle filtering state estimation and smoothed residual for RAIM.