|Abstract:||Urban integrity monitoring using GNSS signals is challenging due to limited satellite visibility and presence of multiple faulty measurements. Traditional Receiver Autonomous Integrity Monitoring (RAIM) approaches separately address state estimation and integrity monitoring, which results in obtaining conservative bounds on integrity risk when the state estimate uncertainty is high in urban environments. Therefore, improving state estimation in urban scenarios is paramount for improved integrity monitoring. Estimating a probability distribution over the state instead of a single point incorporates more information from the observed measurements and aids with better subsequent state estimations. This distribution can be highly multi-modal in complex scenarios. However, most existing integrity monitoring algorithms do not incorporate information from an arbitrary probability distribution in deriving bounds over integrity risk or protection levels. We propose Particle RAIM that uses a modified particle filter to jointly perform state estimation and integrity monitoring over GNSS ranging measurements using probabilistic modelling. Unlike traditional integrity monitoring approaches, Particle RAIM addresses multiple hypotheses associated with correctness of different measurement subsets by incorporating them directly into the state distribution represented by particles. Our designed algorithm infers overall confidence parameters associated with each measurement using a local RAIM-based voting scheme and Bayesian Expectation Maximization. We leverage the generalization error bounds from learning theory to evaluate the integrity risk bound associated with the estimated posterior distribution. We performed experimental verification of our framework under different noise profiles and demonstrated that the proposed algorithm is capable of accurate localization in various fault scenarios. Furthermore, statistical analysis across multiple simulations suggests that the derived integrity risk bound provides a low failure rate upper-bound on the probability of hazardously misleading information.|
Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)
September 16 - 20, 2019
Hyatt Regency Miami
|Pages:||811 - 826|
|Cite this article:||
Gupta, Shubh, Gao, Grace Xingxin, "Particle RAIM for Integrity Monitoring," Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, Florida, September 2019, pp. 811-826.
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