| Paper: | SPTM-P3.6 | ||
| Session: | System Modeling, Representation and Identification | ||
| Time: | Tuesday, May 16, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: System Modeling, Representation, and Identification | ||
| Title: | Particle Filter as a Controlled Markov Chain for On-Line Parameter Estimation in General State Space Models | ||
| Authors: | George Poyiadjis, Sumeetpal S. Singh, University of Cambridge, United Kingdom; Arnaud Doucet, University of British Columbia, Canada | ||
| Abstract: | In this paper we present a novel optimization method for on-line maximum likelihood estimation 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. | ||
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