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

Adyasha Mohanty, Shubh Gupta, Grace Xingxin Gao

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

External IDs:doi:10.1002/navi.455