Linear minimum‐mean‐square error estimation of Markovian jump linear systems with Stochastic coefficient matrices. Issue 12 (1st August 2014)

• Record Type: Journal Article
• Authors: Yang, Yanbo; Liang, Yan; Pan, Quan; Qin, Yuemei; Yang, Feng
• Is Part Of: IET control theory & applications. Volume 8:Issue 12(2014)
• Page Start: 1112
• Page End: 1126
• Publication Date: 2014-08-01
• Subjects: linear systems -- least mean squares methods -- matrix algebra -- state estimation -- discrete time filters -- Markov processes -- stability -- recursive estimation -- target tracking -- sensor fusion; linear minimum mean square error estimation -- stochastic coefficient matrix -- state estimation problem -- discrete time Markovian jump linear systems -- clutter -- orthogonality principle -- sufficient condition -- stability -- LMSCE estimator -- recursive process -- manoeuvering target tracking -- multiple model probabilistic data association filter; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2013.0936
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : This study presents the state estimation problem of discrete‐time Markovian jump linear systems with stochastic coefficient matrices which is motivated by the idea of establishing the general filter framework of the joint state estimation and data association in clutters for tracking the manoeuvering target. According to the orthogonality principle, the linear minimum‐mean‐square error estimator for this system (abbreviated as LMSCE estimator) is derived recursively and sufficient conditions are given for the stability of the LMSCE estimator. The simulation about tracking the manoeuvering target in clutters shows that the LMSCE estimator obtains much more accurate estimate than the well‐known interacting multiple model probabilistic data association filter.