Robust Particle Filtering With State Transition Uncertainties: A Student’s t Disturbance Observer-Based Approach

Abstract

This article is concerned with robust particle filtering (PF) for nonlinear systems with state transition uncertainties. The uncertainty under consideration is characterized in part by an additive uncertainty term (AUT) in the state transition equation and in part by the parametric uncertainty in the probability distribution of process noises. To maintain the sampling efficiency of the PF algorithm in the presence of AUT, the disturbance observer (DO)-based PF approach is adopted, which consists of the DO for real-time AUT reconstruction and a modified PF with AUT-compensated sampling. Instead of using the traditional Kalman filter-based DO, a novel student’s $t$ DO is proposed to handle uncertain noise parameters and achieve robust disturbance estimation. The enhanced robustness of the proposed method, named student’s $t$ DO-based robust PF , has been demonstrated via numerical example. Furthermore, experimental tests on the skylight polarization-aided attitude and heading reference system show that the standard deviation of our method decreases by over 40% as compared with the conventional PF.