Subgradient-based Markov Chain Monte Carlo particle methods for discrete-time nonlinear filtering

Abstract : This work shows how a carefully designed instrumental distribution can improve the performance of a Markov chain Monte Carlo (MCMC) filter for systems with a high state dimension. We propose a special subgradient-based kernel from which candidate moves are drawn. This facilitates the implementation of the filtering algorithm in high dimensional settings using a remarkably small number of particles. We demonstrate our approach in solving a nonlinear non-Gaussian high-dimensional problem in comparison with a recently developed block particle filter and over a dynamic compressed sensing (l1 constrained) algorithm. The results show high estimation accuracy.
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https://hal-imt.archives-ouvertes.fr/hal-01238925
Contributeur : François Septier <>
Soumis le : lundi 7 décembre 2015 - 12:16:07
Dernière modification le : mardi 3 juillet 2018 - 11:32:26

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Avishy Carmi, Lyudmila Mihaylova, François Septier. Subgradient-based Markov Chain Monte Carlo particle methods for discrete-time nonlinear filtering. Signal Processing, Elsevier, 2016, 120, pp.532-536. 〈10.1016/j.sigpro.2015.10.015〉. 〈hal-01238925〉

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