Bayesian State Estimation Using Constrained Zonotopes

Lenka Kuklišová Pavelková

2023

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.

Paper Citation

in Harvard Style

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, SciTePress, pages 189-194. DOI: 10.5220/0012230900003543

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Kuklišová Pavelková L.
PY - 2023
SP - 189
EP - 194
DO - 10.5220/0012230900003543
PB - SciTePress