Particle filter for sensor fusion in a land vehicle navigation system

Abstract

Generally, the extended Kalman filter (EKF) is used for sensor fusion in a land vehicle navigation system. However, defects of the first-order linearization of the nonlinear model in the EKF can introduce large estimated errors, and may lead to sub-optimal performance. In order to yield higher accuracy of navigation, in this paper, a novel particle filter (PF) for sensor fusion is proposed and the sampling importance resampling particle filter (SIR-PF) is applied to address the nonlinear measurement model and it shows better performances when compared with the EKF. The basic theories and application of the general PF and the SIR-PF for a global position system/dead reckoning (GPS/DR) integrated navigation system are discussed.