Title: A Bayesian Nonparametrics based Robust Particle Filter Algorithm,
Abstract: This paper is concerned with the online estimation of a nonlinear dynamic
system from a series of noisy measurements. The focus is on cases wherein
outliers are present in-between normal noises. We assume that the outliers
follow an unknown generating mechanism which deviates from that of normal
noises, and then model the outliers using a Bayesian nonparametric model called
Dirichlet process mixture (DPM). A sequential particle-based algorithm is
derived for posterior inference for the outlier model as well as the state of
the system to be estimated. The resulting algorithm is termed DPM based robust
PF (DPM-RPF). The nonparametric feature makes this algorithm allow the data to
"speak for itself" to determine the complexity and structure of the outlier
model. Simulation results show that it performs remarkably better than two
state-of-the-art methods especially when outliers appear frequently along time.