Bayesian nonparametric state and impulsive measurement noise density estimation in nonlinear dynamic systems

In this paper, we address the problem of online state and measurement 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.