Bayesian State Estimation Using Constrained Zonotopes

Lenka Kuklišová Pavelková

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

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.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 3.144.42.233

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

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