A particle‐filtering framework for integrity risk of GNSS‐camera sensor fusion

Abstract Adopting a joint approach toward state estimation and integrity monitoring results in unbiased unlike traditional approaches. So far, was used particle RAIM (Gupta & Gao, 2019) for GNSS measurements only. In our work, we extend Particle to GNSS-camera fused system monitoring. To account vision faults, derived probability distribution over position from camera images using map-matching. We formulated Kullback-Leibler divergence (Kullback Leibler, 1951) metric assess the consistency of mitigate faults during sensor fusion. Experimental validation on real-world data set shows that algorithm produces less than 11 m error risk bounds HMI with 0.11 failure rate an 8 alert limit urban scenario.