Title :
A numerical method for state estimation of continuous time Markov jump linear systems
Author :
Costa, Eduardo F. ; de Saporta, Benoite
Author_Institution :
Inst. de Cienc. Mat. e de Comput., Univ. Sao Paulo, Sao Carlos, Brazil
Abstract :
This paper introduces an approximation procedure for implementing the Kalman-Bucy filter (KBF) for continuous-time Markov jump linear systems with perfect observation of the jump variable. The procedure involves the discretization of the jump times, which is performed using a quantization approach. It allows for a pre computation of the gain matrices of the KBF. We develop an error analysis indicating that error covariance of the proposed filter approaches the error covariance of the KBF, which is the optimal one for the considered estimation problem. A numerical example is included to illustrate the implementation and the performance of the approximating filter.
Keywords :
Kalman filters; approximation theory; continuous time systems; error analysis; linear systems; matrix algebra; state estimation; stochastic systems; KBF; Kalman-Bucy filter; approximation procedure; continuous time Markov jump linear systems; error analysis; error covariance; gain matrices; jump times discretization; jump variable; numerical method; quantization approach; state estimation; Approximation methods; Kalman filters; Linear systems; Markov processes; Quantization (signal); Riccati equations; Trajectory;
Conference_Titel :
Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
Conference_Location :
Los Angeles, CA
Print_ISBN :
978-1-4799-7746-8
DOI :
10.1109/CDC.2014.7040388