# 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.

Download#### 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