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Approximate Bayesian Prediction Using State Space Model with Uniform Noise

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Informatics in Control, Automation and Robotics (ICINCO 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 613))

Abstract

This paper proposes a one-step-ahead Bayesian output predictor for the linear stochastic state space model with uniformly distributed state and output noises. A model with discrete-time inputs, outputs and states is considered. The model matrices and noise parameters are supposed to be known. Unknown states are estimated using the Bayesian approach. The complex polytopic support of the posterior probability density function (pdf) is approximated by a parallelotopic set. The state estimation consists of two stages, namely the time and data update including the mentioned approximation. The output prediction is performed as an interstep between the time update and the data update. The behaviour of the proposed algorithm is illustrated by simulations and compared with the Kalman filter.

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Author information

Authors and Affiliations

  1. Institute of Information Theory and Automation, Czech Academy of Sciences, Prague, Czech Republic

    Ladislav Jirsa, Lenka Kuklišová Pavelková & Anthony Quinn

  2. Department of Electronic and Electrical Engineering, University of Dublin, Dublin, Ireland

    Anthony Quinn

Authors
  1. Ladislav Jirsa
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  2. Lenka Kuklišová Pavelková
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  3. Anthony Quinn
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Corresponding author

Correspondence to Lenka Kuklišová Pavelková .

Editor information

Editors and Affiliations

  1. Ford Research and Advanced Engineer, Dearborn, MI, USA

    Oleg Gusikhin

  2. University of Paris-EST Créteil (UPEC), Créteil, France

    Kurosh Madani

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Jirsa, L., Kuklišová Pavelková, L., Quinn, A. (2020). Approximate Bayesian Prediction Using State Space Model with Uniform Noise. In: Gusikhin, O., Madani, K. (eds) Informatics in Control, Automation and Robotics. ICINCO 2018. Lecture Notes in Electrical Engineering, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-030-31993-9_27

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