Bayesian State Estimation Using Constrained Zonotopes

0578233 - ÚTIA 2024 RIV PT eng C - Konferenční příspěvek (zahraniční konf.)
Kuklišová Pavelková, Lenka
Bayesian State Estimation Using Constrained Zonotopes.
Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics (ICINCO). Setúbal: SCITEPRESS, 2023 - (Gini, G.; Nijmeijer, H.; Filev, D.), s. 189-194. ISBN 978-989-758-670-5. ISSN 2184-2809.
[International Conference on Informatics in Control, Automation and Robotics 2023 (ICINCO 2023) /20./. Řím (IT), 13.11.2023-15.11.2023]
Grant CEP: GA ČR(CZ) GC23-04676J
Institucionální podpora: RVO:67985556
Klíčová slova: stochastic systems * recursive state estimation * bounded noise * constrained zonotope * state-space model * linear system * approximate estimation
Obor OECD: Automation and control systems
http://library.utia.cas.cz/separaty/2023/AS/kuklisova-0578233.pdf

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.
Trvalý link: https://hdl.handle.net/11104/0347646