Stationary filter for continuous-time Markovian jump linear systems

被引:28
|
作者
Fragoso, MD
Rocha, NCS
机构
[1] CNPq, LNCC, Natl Lab Sci Comp, BR-25651070 Rio De Janeiro, Brazil
[2] Univ Fed Rio de Janeiro, Inst Matemat, Rio De Janeiro, Brazil
关键词
Kalman filter; Riccati equation; jump systems; Markov parameters;
D O I
10.1137/S0363012903436259
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We derive a stationary filter for the best linear mean square filter (BLMSF) of continuous-time Markovian jump linear systems (MJLS). It amounts here to obtain the convergence of the error covariance matrix of the BLMSF to a stationary value under the assumption of mean square stability of the MJLS and ergodicity of the associated Markov chain theta(t). It is shown that there exists a unique solution for the stationary Riccati filter equation, and this solution is the limit of the error covariance matrix of the BLMSF. The advantage of this scheme is that it is easy to implement since the filter gain can be performed offline, leading to a linear time-invariant filter.
引用
收藏
页码:801 / 815
页数:15
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