State estimation of jump Markov linear systems via stochastic sampling algorithms

Author

Doucet, Arnaud ; Logothetis, Andrew ; Krishnamurthy, Vikram

Author_Institution

Dept. of Eng., Cambridge Univ., UK

Volume

2

fYear

1998

fDate

16-18 Dec 1998

Firstpage

2305

Abstract

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

Keywords

Markov processes; discrete time systems; linear systems; sampling methods; simulated annealing; state estimation; stochastic systems; MAP sequence estimate; Metropolis-Hastings scheme; conditional mean state estimates; data augmentation scheme; finite state Markov chain; jump Markov linear systems; stochastic annealing; stochastic sampling algorithms; Control systems; Costs; Linear systems; Sampling methods; Signal processing algorithms; State estimation; Stochastic processes; Stochastic systems; Yield estimation; Yttrium;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1998. Proceedings of the 37th IEEE Conference on

Conference_Location

Tampa, FL

ISSN

0191-2216

Print_ISBN

0-7803-4394-8

Type

conf

DOI

10.1109/CDC.1998.758688

Filename

758688