Approximate Recursive Bayesian Estimation of State Space Model with Uniform Noise

Lenka Pavelková, Ladislav Jirsa

2018

Abstract

This paper proposes a recursive algorithm for the state estimation of a linear stochastic state space model. A model with discrete-time inputs, outputs and states is considered. The model matrices are supposed to be known. A noise of the involved model is described by a uniform distribution. The states are estimated using Bayesian approach. Without using an approximation, the complexity of the posterior probability density function (pdf) increases with time. The paper proposes an approximation of this complex pdf so that a feasible support of the posterior pdf is kept during the estimation. The state estimation consists of two stages, namely the time and data update including the mentioned approximation. The behaviour of the proposed algorithm is illustrated by simulations and compared with other methods.

Paper Citation

in Harvard Style

Pavelková L. and Jirsa L. (2018). Approximate Recursive Bayesian Estimation of State Space Model with Uniform Noise.In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-321-6, pages 388-394. DOI: 10.5220/0006933803880394

in Bibtex Style

@conference{icinco18,
author={Lenka Pavelková and Ladislav Jirsa},
title={Approximate Recursive Bayesian Estimation of State Space Model with Uniform Noise},
booktitle={Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2018},
pages={388-394},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006933803880394},
isbn={978-989-758-321-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Approximate Recursive Bayesian Estimation of State Space Model with Uniform Noise
SN - 978-989-758-321-6
AU - Pavelková L.
AU - Jirsa L.
PY - 2018
SP - 388
EP - 394
DO - 10.5220/0006933803880394