State and impulsive time-varying measurement noise density estimation in nonlinear dynamic systems using Dirichlet Process Mixtures

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.