DocumentCode
455044
Title
Particle Filter as A Controlled Markov Chain For On-Line Parameter Estimation in General State Space Models
Author
Poyiadjis, George ; Singh, Sumeetpal S. ; Doucet, Arnaud
Author_Institution
Dept. of Eng., Cambridge Univ.
Volume
3
fYear
2006
fDate
14-19 May 2006
Abstract
In this paper we present a novel optimization method for on-line maximum likelihood estimation (MLE) of the static parameters of a general state space model. Our approach is based on viewing the particle filter as a controlled Markov chain, where the control is the unknown static parameters to be identified. The algorithm relies on the computation of the gradient of the particle filter using a score function approach
Keywords
Markov processes; maximum likelihood estimation; particle filtering (numerical methods); state-space methods; controlled Markov chain; general state space models; on-line maximum likelihood estimation; on-line parameter estimation; optimization method; particle filter; score function approach; Filtering; Hidden Markov models; Maximum likelihood estimation; Optimization methods; Parameter estimation; Particle filters; Sliding mode control; State estimation; State-space methods; Statistics;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location
Toulouse
ISSN
1520-6149
Print_ISBN
1-4244-0469-X
Type
conf
DOI
10.1109/ICASSP.2006.1660657
Filename
1660657
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