Novel Snapshot Integrity Algorithm for Automotive Applications: Test Results Based on Real Data

R. Bryant, O. Julien, C. Hide, S. Moridi, I. Sheret

Abstract: This paper describes a novel automotive snapshot integrity algorithm for bounding position, based on modelling GNSS measurements with non-Gaussian error distributions. A Bayesian method is used to derive the posterior probability distribution on position given a set of pseudorange and carrier phase observations from a single epoch. MCMC is then used to obtain rigorous probabilistic bounds on position. The MCMC method uses a novel form of parallel tempering to properly sample the multimodal posterior distribution created by carrier phase integer ambiguities, and importance sampling to obtain faster than real-time computational performance. Experimental results based on 27 hours of road driving show that integrity is maintained properly, with bounds which are significantly tighter than a more conventional EKF approach.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 670 - 681
Cite this article: Bryant, R., Julien, O., Hide, C., Moridi, S., Sheret, I., "Novel Snapshot Integrity Algorithm for Automotive Applications: Test Results Based on Real Data," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 670-681.
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