DocumentCode :
744031
Title :
Recursive Bayesian estimation for Markov jump linear systems with unknown mode-dependent state delays
Author :
Shunyi Zhao ; Fei Liu
Author_Institution :
Key Lab. of Adv. Process Control for Light Ind. (Minist. of Educ.), Jingnan Univ., Wuxi, China
Volume :
7
Issue :
9
fYear :
2013
Firstpage :
911
Lastpage :
919
Abstract :
This study considers the minimum mean square error estimation problem for a class of jump Markov linear systems with unknown mode-dependent state delays. In order to show the difficulties caused by the unknown delays, the online Bayesian equation of the investigated system is firstly developed by incorporating the time-delay estimation into the recursion of system states. However, computing such optimal estimation causes an exponential increase in the requirement of computation and storage load. Therefore two different approximation techniques: interacting multiple-model approximation and detection-estimation method are utilised to obtain two suboptimal but executable filtering algorithms, respectively. Simulation results of the proposed methods for a system are presented to illustrate the effectiveness.
Keywords :
Markov processes; delay estimation; filtering theory; least mean squares methods; recursive estimation; Markov jump linear systems; approximation technique; detection-estimation method; interacting multiple-model approximation; minimum mean square error estimation problem; online Bayesian equation; optimal estimation; recursive Bayesian estimation; storage load; suboptimal executable filtering algorithm; system state recursion; time-delay estimation; unknown mode-dependent state delays;
fLanguage :
English
Journal_Title :
Signal Processing, IET
Publisher :
iet
ISSN :
1751-9675
Type :
jour
DOI :
10.1049/iet-spr.2013.0012
Filename :
6670924
Link To Document :
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