Iterative algorithms for state estimation of jump Markov linear systems

A Doucet, C Andrieu

Research output: Contribution to journalArticle (Academic Journal)peer-review

84 Citations (Scopus)

Abstract

Jump Markov linear systems (JMLSs) are linear systems whose parameters evolve with time according to a finite state Markov chain, Given a set of observations, our aim is to estimate the states of the finite state Markov chain and the continuous (in space) states of the linear system. In this paper, we present original deterministic and stochastic iterative algorithms for optimal state estimation of JMLSs, The first stochastic algorithm yields minimum mean square error (MMSE) estimates of the finite state space Markov chain and of the continuous state of the JMLS, A deterministic and a stochastic algorithm are given to obtain the marginal maximum a posteriori (MMAP) sequence estimate of the finite state Markov chain. Finally, a deterministic and a stochastic algorithm are derived to obtain the MMAP sequence estimate of the continuous state of the JMLS, Computer simulations are carried out to evaluate the performance of the proposed algorithms. The problem of deconvolution of Bernoulli-Gaussian (BG) processes and the problem of tracking a maneuvering target are addressed.
Translated title of the contributionIterative algorithms for state estimation of jump Markov linear systems
Original languageEnglish
Pages (from-to)1216 - 1227
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume49 (6)
Publication statusPublished - Jun 2001

Bibliographical note

Publisher: IEEE - Inst \electrical Elecronic Engineers Inc
Other identifier: IDS number 434KR

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