STATE AND IMPULSIVE TIME-VARYING MEASUREMENT NOISE DENSITY ESTIMATION IN NONLINEAR DYNAMIC SYSTEMS USING DIRICHLET PROCESS MIXTURES

被引:0
作者
Jaoua, Nouha [1 ]
Septier, Francois [1 ]
Duflos, Emmanuel [1 ]
Vanheeghe, Philippe [1 ]
机构
[1] CNRS, LAGIS, UMR 8219, F-59651 Villeneuve Dascq, France
来源
2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2014年
关键词
Bayesian nonparametric; Time-Varying Dirichlet Process Mixture; impulsive noise; alpha-stable process; particle filter; MODELS;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
In this paper, we focus on the challenging task of the online estimation 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.
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页数:5
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