Causal and strictly causal estimation for jump linear systems: An LMI analysis

被引:3
|
作者
Fletcher, Alyson K. [1 ]
Rangan, Sundeep [2 ]
Goyal, Vivek K. [3 ]
Ramchandran, Kannan [1 ]
机构
[1] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA
[2] Flarion Technol, Bedminster, NJ 07921 USA
[3] MIT, Dept Elect Engn & Comp Sci, Cambridge, MA 02139 USA
来源
2006 40TH ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS, VOLS 1-4 | 2006年
关键词
jump linear systems; state estimation; Kalman filtering;
D O I
10.1109/CISS.2006.286665
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Jump linear systems are linear state-space systems with random time variations driven by a finite Markov chain. These models are widely used in nonlinear control, and more recently, in the study of communication over lossy channels. This paper considers a general jump linear estimation problem of estimating an unknown signal from an observed signal, where both Signals are described as outputs of a jump linear system. A bound on the minimum achievable estimation error in terms of linear matrix inequalities (LMIs) is presented, along with a simple jump linear estimator that achieves this bound. While previous analysis has considered only the strictly causal estimation problem, this work presents both strictly causal and causal solutions.
引用
收藏
页码:1302 / 1307
页数:6
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