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Monography Chapter

Approximate Bayesian Prediction Using State Space Model with Uniform Noise

Jirsa Ladislav, Kuklišová Pavelková Lenka, Quinn Anthony

: Informatics in Control, Automation and Robotics : 15th International Conference, ICINCO 2018, Porto, Portugal, July 29-31, 2018, Revised Selected Papers, p. 552-568 , Eds: Gusikhin O., Madani K.

: GA18-15970S, GA ČR

: stochastic state space model, observation prediction, Bayesian state space estimation, uniform noise

: 10.1007/978-3-030-31993-9

: http://library.utia.cas.cz/separaty/2019/AS/pavelkova-0511101.pdf

(eng): 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,\noutputs and states is considered. The model matrices and noise parameters are supposed to be known. Unknown states are estimated using Bayesian approach. A complex polytopic support of 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 inter-step between the time update and the data update. The behaviour of the proposed algorithm is illustrated by simulations and compared with Kalman filter.

: BC

: 10201