THE SMOOTH PARTICLE VARIABLE STRUCTURE FILTER

Abstract

In this paper, a new state and parameter estimation method is introduced based on the particle filter (PF) and the smooth variable structure filter (SVSF). The PF is a popular estimation method, which makes use of distributed point masses to form an approximation of the probability distribution function (PDF). The SVSF is a relatively new estimation strategy based on sliding mode concepts, formulated in a predictorcorrector format. It has been shown to be very robust to modeling errors and uncertainties. The combined method, referred to as the smooth particle variable structure filter (SPVSF), utilizes the estimates and state error covariance of the SVSF to formulate the proposal distribution which generates the particles used by the PF. The SPVSF method is applied on two computer experiments, namely a nonlinear target tracking scenario and estimation of electrohydrostatic actuator parameters. The results are compared with other popular Kalman-based estimation methods.