State and Impulsive Time-Varying Measurement Noise Density Estimation in Nonlinear Dynamic Systems Using Dirichlet Process Mixtures

Nouha Jaoua 1 François Septier 1 Emmanuel Duflos 1 Philippe Vanheeghe 1
1 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : In this paper, we focus on the challenging task of the online esti- mation of the state and the unknown measurement noise density in nonlinear dynamic state-space models. We are especially interested in making inference in the presence of impulsive and time-varying noise. A flexible Bayesian nonparametric noise model based on an extension of Dirichlet Process Mixtures, namely the Time Varying Dirichlet process Mixtures, is introduced. A novel method based on sequential Monte Carlo methods is proposed to perform the optimal online estimation. Simulation results demonstrate the efficiency and the robustness of this method.
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2014, Florence, Italy. pp.330 - 334, 2014
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https://hal-imt.archives-ouvertes.fr/hal-01007529
Contributeur : François Septier <>
Soumis le : lundi 16 juin 2014 - 18:08:22
Dernière modification le : jeudi 11 janvier 2018 - 06:26:40

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  • HAL Id : hal-01007529, version 1

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Nouha Jaoua, François Septier, Emmanuel Duflos, Philippe Vanheeghe. State and Impulsive Time-Varying Measurement Noise Density Estimation in Nonlinear Dynamic Systems Using Dirichlet Process Mixtures. IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2014, Florence, Italy. pp.330 - 334, 2014. 〈hal-01007529〉

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