0519515 20240103223415.720200113235959.9 85081289223 10.1109/ISSPIT47144.2019.9001829 6 s. P cav_un_epca*0519792978-1-7281-5341-4PiscatawayIEEE2019 Bayesian filtering uniform distribution on a parallelotopic support (UPS) local approximation Kullback-Leibler divergence cav_un_auth*0101119 Jirsa Ladislav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0382598 Kuklišová Pavelková Lenka UTIA-B Adaptivní systémy Adaptive Systems AS AS Department of Adaptive Systems CZ K Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0370768 Quinn Anthony UTIA-B Adaptivní systémy Adaptive Systems AS AS Department of Adaptive Systems IE Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2019/AS/jirsa-0519515.pdf cav_un_auth*0362986 GA18-15970S GA ČR CZ 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. cav_un_auth*0387099 IEEE International Symposium on Signal Processing and Information Technology 2019 (ISSPIT 2019) 20191210 20191212 Ajman AE BB 20000 20200 20205 2020 3 PR RVO:67985556 http://hdl.handle.net/11104/0304778 1 cav_un_auth*0387100 Trinity College, Dublin TCD IE S 2019 85081289223 SCOPUS PUBMED WOS cav_un_epca*0519792 Proceedings of the IEEE International Symposium on Signal Processing and Information Technology 2019 (ISSPIT 2019) 978-1-7281-5341-4 Piscataway IEEE 2019