State estimation of jump Markov linear systems via stochastic sampling algorithms

TitleState estimation of jump Markov linear systems via stochastic sampling algorithms
Publication TypeConference Paper
Year of Publication1998
AuthorsDoucet, A., A. Logothetis, and V. Krishnamurthy
Conference NameDecision and Control, 1998. Proceedings of the 37th IEEE Conference on
Pagination2305 -2310 vol.2
Date Publisheddec.
Keywordsconditional mean state estimates, data augmentation scheme, discrete time systems, finite state Markov chain, jump Markov linear systems, linear systems, MAP sequence estimate, Markov processes, Metropolis-Hastings scheme, sampling methods, simulated annealing, state estimation, stochastic annealing, stochastic sampling algorithms, stochastic systems

We present three algorithms based on stochastic sampling methods for state estimation of jump Markov linear systems. The cost per iteration is linear in the data length. The first proposed algorithm is a data augmentation (DA) scheme that yields conditional mean state estimates. The second proposed scheme is a stochastic annealing (SA) version of DA that computes the joint MAP sequence estimate of the finite and continuous states. Finally, a Metropolis-Hastings DA scheme based on SA is designed to yield the MAP estimate of the finite state Markov chain, is proposed. Convergence results of the three above mentioned stochastic algorithms are obtained


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