The Gaussian Particle Filter for Diagnosis of Non-linear Systems

Abstract: Fault diagnosis is a critical task for autonomous operation of systems such as spacecraft and planetary rovers, and must often be performed on-board. Unfortunately, these systems frequently also have relatively little computational power to devote to diagnosis. For this reason, algorithms for these applications must be extremely efficient, and preferably anytime. In this paper we introduce the Gaussian particle filter (GPF), a new variant on the particle filtering algorithm specifically for non-linear hybrid systems. Each particle samples a discrete mode and approximates the continuous variables by a multivariate Gaussian that is updated at each time-step using an unscented Kalman filter. The algorithm is closely related to Rao-Blackwellized Particle Filtering and equally efficient, but is more broadly applicable. We demonstrate the algorithm on a Mars rover problem and show that it is faster and more accurate than traditional particle filters.