Approximate Bayesian Prediction Using State Space Model with Uniform Noise

0511101 20250123090155.320191117235959.9 85075680374 000612994900027 10.1007/978-3-030-31993-9 Approximate Bayesian Prediction Using State Space Model with Uniform Noise 17 s. 570 P cav_un_epca*0517111978-3-030-31992-21876-1100Informatics in Control, Automation and Robotics : 15th International Conference, ICINCO 2018, Porto, Portugal, July 29-31, 2018, Revised Selected Papers552-568ChamSpringer2019GusikhinO.MadaniK. stochastic state space model observation prediction Bayesian state space estimation uniform noise cav_un_auth*0101119 Jirsa Ladislav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0382598 Kuklišová Pavelková Lenka UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0370768 Quinn Anthony UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems IE Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2019/AS/pavelkova-0511101.pdf GA18-15970S GA ČR CZ cav_un_auth*0362986 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 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 10000 10200 10201 2020 3 RVO:67985556 http://hdl.handle.net/11104/0302396 S 2019 85075680374 SCOPUS PUBMED 000612994900027 WOS cav_un_epca*0517111 Informatics in Control, Automation and Robotics : 15th International Conference, ICINCO 2018, Porto, Portugal, July 29-31, 2018, Revised Selected Papers 978-3-030-31992-2 1876-1100 552 568 Cham Springer 2019 Lecture Notes in Electrical Engineering 613 Gusikhin O. 340 340 Madani K.