Bayesian Nonparametric State and Impulsive Measurement Noise Density Estimation in Nonlinear Dynamic Systems

Nouha Jaoua 1 Emmanuel Duflos 1 Philippe Vanheeghe 1 François Septier 1
1 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : In this paper, we address the problem of online state and measure- ment noise density estimation in nonlinear dynamic state-space models. We are especially interested in making inference in the presence of impulsive and multimodal noise. The proposed method relies on the introduction of a flexible Bayesian nonparametric noise model based on Dirichlet Process mixtures. A novel approach based on sequential Monte Carlo methods is proposed to perform the optimal online estimation. Simulation results demonstrate the efficiency and the robustness of this approach.
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2013, Vancouver, Canada. pp.5755 - 5759, 2013
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https://hal-imt.archives-ouvertes.fr/hal-00813185
Contributeur : François Septier <>
Soumis le : lundi 15 avril 2013 - 11:23:12
Dernière modification le : jeudi 11 janvier 2018 - 02:05:49

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  • HAL Id : hal-00813185, version 1

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Nouha Jaoua, Emmanuel Duflos, Philippe Vanheeghe, François Septier. Bayesian Nonparametric State and Impulsive Measurement Noise Density Estimation in Nonlinear Dynamic Systems. IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2013, Vancouver, Canada. pp.5755 - 5759, 2013. 〈hal-00813185〉

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