期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 49, 期 3, 页码 613-624出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/78.905890
关键词
filtering theory; Monte Carlo methods; state estimation; switching systems
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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