Authors:
Lenka Pavelková
and
Ladislav Jirsa
Affiliation:
Institute of Information Theory and Automation, The Czech Academy of Sciences, Pod Vodárenskou věží 4, Prague and Czech Republic
Keyword(s):
State Estimation, State-space Models, Linear Systems, Bounded Noise, Probabilistic Models, Approximate Estimation, Recursive Estimation.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
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.