Particle filters for state estimation of jump Markov linear systems

TitleParticle filters for state estimation of jump Markov linear systems
Publication TypeJournal Article
Year of Publication2001
AuthorsDoucet, A., N. J. Gordon, and V. Krishnamurthy
JournalSignal Processing, IEEE Transactions on
Pagination613 -624
Date Publishedmar.
Keywordscomputer simulations, deconvolution, digital simulation, filtering theory, finite state Markov chain, importance sampling, impulsive processes, jump Markov linear systems, linear systems, maneuvering target tracking, Markov chain Monte Carlo methods, Markov processes, on-line deconvolution, optimal filtering, optimal fixed-lag smoothing, optimal state estimates, optimisation, particle filters, performance evaluation, sequential importance sampling, simulation-based algorithms, state estimation, statistical structure, tracking, variance reduction methods

Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. In this paper, our aim is to recursively compute optimal state estimates for this class of systems. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem. Our algorithms combine sequential importance sampling, a selection scheme, and Markov chain Monte Carlo methods. They use several variance reduction methods to make the most of the statistical structure of JMLS. Computer simulations are carried out to evaluate the performance of the proposed algorithms. The problems of on-line deconvolution of impulsive processes and of tracking a maneuvering target are considered. It is shown that our algorithms outperform the current methods


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