0578233 20240402214741.720231120235959.9 10.5220/0012230900003543 6 s. P cav_un_epca*0578232978-989-758-670-52184-2809189-194SetúbalSCITEPRESS2023GiniGiuseppinaNijmeijerHenkFilevDimitar stochastic systems recursive state estimation bounded noise constrained zonotope state-space model linear system approximate estimation cav_un_auth*0382598 Kuklišová Pavelková Lenka UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2023/AS/kuklisova-0578233.pdf GC23-04676J GA ČR CZ cav_un_auth*0453493 This paper proposes an approximate Bayesian recursive algorithm for the state estimation of a linear discrete-time stochastic state-space model. The involved state and observation noises are assumed to be bounded and uniformly distributed. The support of a posterior probability density function (pdf) is approximated by a constrained zonotope of an adjustable complexity. The behaviour of the proposed algorithm is illustrated by simulations and compared with other methods. cav_un_auth*0458148 International Conference on Informatics in Control, Automation and Robotics 2023 (ICINCO 2023) /20./ 20231113 20231115 Řím IT BC 20000 20200 20205 2024 1 PR RVO:67985556 https://hdl.handle.net/11104/0347646 S 5 25 1 13 2023 SCOPUS PUBMED WOS cav_un_epca*0578232 Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics (ICINCO) SCITEPRESS 2023 Setúbal 189 194 978-989-758-670-5 2184-2809 Gini Giuseppina 340 Nijmeijer Henk 340 Filev Dimitar 340