Titre :

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

Auteur(s) :

Jaoua, Nouha [Auteur]
LAGIS-SI
Duflos, Emmanuel [Auteur]
LAGIS-SI
Vanheeghe, Philippe [Auteur]
LAGIS-SI
Septier, Francois [Auteur]
LAGIS-SI

Titre de la manifestation scientifique :

IEEE International Conference on Acoustics, Speech, and Signal Processing

Ville :

Vancouver

Pays :

Canada

Date de début de la manifestation scientifique :

2013-05-26

Date de publication :

2013-05-26

Mot(s)-clé(s) en anglais :

Bayesian nonparametric
Dirichlet Process Mixture
particle filter
impulsive noise
α-stable process

Discipline(s) HAL :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL