Stochastic Sampling Algorithms for State Estimation of Jump Markov Linear Systems (2000)
| Venue: | IEEE Transactions on Automatic Control |
| Citations: | 19 - 2 self |
BibTeX
@ARTICLE{Doucet00stochasticsampling,
author = {Arnaud Doucet and Andrew Logothetis and Vikram Krishnamurthy},
title = {Stochastic Sampling Algorithms for State Estimation of Jump Markov Linear Systems},
journal = {IEEE Transactions on Automatic Control},
year = {2000},
volume = {45},
pages = {200--0}
}
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Abstract
Jump Markov linear systems 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. The computational cost in computing conditional mean or maximum a posteriori (MAP) state estimates of the Markov chain or the state of the jump Markov linear system grows exponentially in the number of observations.
