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.
Complete list of metadatas

https://hal-imt.archives-ouvertes.fr/hal-01007529
Contributor : François Septier <>
Submitted on : Monday, June 16, 2014 - 6:08:22 PM
Last modification on : Thursday, February 21, 2019 - 10:34:10 AM

Identifiers

  • HAL Id : hal-01007529, version 1

Citation

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. ⟨hal-01007529⟩

Share

Metrics

Record views

246