Bibliografie

(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.