State Estimation and Mode Detection for Stochastic Hybrid System

Abstract

A central issue in real time applications of particle filtering is high computational cost. This problem is particularly compounded when particle filters are used in hybrid system estimation and especially in algorithms based on the interacting multiple model (IMM) algorithm. In this paper a new method for nonlinear/non-Gaussian Markovian switching system state estimation is proposed. The new method combines IMMPF (IMM particle filtering) with ideas from OTPF (observation and transition-based most likely modes tracking particle filtering) in order to get high accuracy estimation with reduced computational load. Simulations are carried out to evaluate the performance of the proposed algorithm. It is shown that the proposed algorithm outperforms OTPF in both accuracy and computation complexity aspect. Compared with IMMPF, the new method performs almost as well as IMMPF but with much lower computational cost.