Kalman Filter Partial Innovation Sequence Monitor

Cagatay Tanil

Peer Reviewed

Abstract:	Timely detection and exclusion of slow faults in an integrated positioning sensor systems are crucial to deliver safe navigation. This paper describes a computationally cheap monitor that utilizes a sequence of a subset (partial) Kalman filter innovation vectors only associated with fault hypotheses. The paper also establishes an analytical recursive formulas to evaluate the partial innovations monitor. Both analytical and Monte Carlo results showed that the partial sequencing is superior to conventional methods using full innovation vector at each time epoch.
Published in:	Proceedings of the 2022 International Technical Meeting of The Institute of Navigation January 25 - 27, 2022 Hyatt Regency Long Beach Long Beach, California
Pages:	1263 - 1272
Cite this article:	Tanil, Cagatay, "Kalman Filter Partial Innovation Sequence Monitor," Proceedings of the 2022 International Technical Meeting of The Institute of Navigation, Long Beach, California, January 2022, pp. 1263-1272. https://doi.org/10.33012/2022.18195
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