0519515 - ÚTIA 2020 RIV US eng C - Conference Paper (international conference)
Jirsa, Ladislav - Kuklišová Pavelková, Lenka - Quinn, Anthony
Bayesian Filtering for States Uniformly Distributed on a Parallelotopic Support.
Proceedings of the IEEE International Symposium on Signal Processing and Information Technology 2019 (ISSPIT 2019). Piscataway: IEEE, 2019. ISBN 978-1-7281-5341-4.
[IEEE International Symposium on Signal Processing and Information Technology 2019 (ISSPIT 2019). Ajman (AE), 10.12.2019-12.12.2019]
R&D Projects: GA ČR(CZ) GA18-15970S
Institutional support: RVO:67985556
Keywords : Bayesian filtering * uniform distribution on a parallelotopic support (UPS) * local approximation * Kullback-Leibler divergence
OECD category: Automation and control systems
http://library.utia.cas.cz/separaty/2019/AS/jirsa-0519515.pdf

This paper contributes to the literature on Bayesian filtering in the case where the processes driving the states and observations are uniformly distributed on finite intervals. We introduce the class of uniform distributions on parallelotopic supports (UPS). We derive optimal local distributional projections (i.e. approximations) within this UPS class-in the sense of minimum Kullback-Leibler divergence-of the outputs of the data and time updates of filtering. We demonstrate that the UPS class provides a tighter approximation (and therefore more precise inferences) than a previously reported approximation on orthotopic supports. It does this, while still achieving bounded complexity in the resulting recursive filtering algorithm. The comparative performance of the UPS-closed filtering algorithm is explored-via both Bayesian and frequentist performance measures-as a function of signal-to-noise ratio and state dimension in a position-velocity system.
Permanent Link: http://hdl.handle.net/11104/0304778