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Convolution particle filtering for parameter estimation in general state-space models

Fabien Campillo 1 Vivien Rossi 1 
1 ASPI - Applications of interacting particle systems to statistics
UR1 - Université de Rennes 1, Inria Rennes – Bretagne Atlantique , CNRS - Centre National de la Recherche Scientifique : UMR6074
Abstract : The state-space modeling of partially observed dynamic systems generally requires estimates of unknown parameters. From a practical point of view, it is relevant in such filtering contexts to simultaneously estimate the unknown states and parameters. Efficient simulation-based methods using convolution particle filters are proposed. The regularization properties of these filters is well suited, given the context of parameter estimation. Firstly the usual non Bayesian statistical estimates are consid- ered: the conditional least squares estimate (CLSE) and the maximum likelihood estimate (MLE). Secondly, in a Bayesian context, a Monte Carlo type method is presented. Finally we present a simulated case study.
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Fabien Campillo, Vivien Rossi. Convolution particle filtering for parameter estimation in general state-space models. Proceedings of the 45th IEEE Conference on Decision and Control, San Diego (USA), Dec 2006, San Diego, United States. pp.2159-2164, ⟨10.1109/CDC.2006.376751⟩. ⟨inria-00506581⟩

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