Bayesian State Estimation Using Constrained Zonotopes

Lenka Kuklišová Pavelková

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Paper

Bayesian State Estimation Using Constrained Zonotopes

Topics: Adaptive Signal Processing and Control; Optimization Problems in Signal Processing; System Identification

In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, 189-194, 2023 , Rome, Italy

Author: Lenka Kuklišová Pavelková

Affiliation: The Czech Academy of Sciences, Institute of Information Theory and Automation Pod Vodarenskou vezi 4, Prague, Czech Republic

Keyword(s): Stochastic Systems, Recursive State Estimation, Bounded Noise, Constrained Zonotope, State-Space Models, Linear Systems, Approximate Estimation.

Abstract: 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.

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Paper citation in several formats:

Kuklišová Pavelková, L. (2023). Bayesian State Estimation Using Constrained Zonotopes. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-670-5; ISSN 2184-2809, SciTePress, pages 189-194. DOI: 10.5220/0012230900003543

@conference{icinco23,
author={Lenka {Kuklišová Pavelková}.},
title={Bayesian State Estimation Using Constrained Zonotopes},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2023},
pages={189-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012230900003543},
isbn={978-989-758-670-5},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Bayesian State Estimation Using Constrained Zonotopes
SN - 978-989-758-670-5
IS - 2184-2809
AU - Kuklišová Pavelková, L.
PY - 2023
SP - 189
EP - 194
DO - 10.5220/0012230900003543
PB - SciTePress