Abstract: A novel particle filter based on the iterated extended kalman is proposed. The iterated extended kalman filter (IEKF) is used to generate the proposal distribution. Because the IEKF can acquire a maximum a posteriori (MAP) estimate of the nonlinear system, and the importance density function integrates the latest observation into system state transition density, so the proposal distribution can approximate the posterior distribution reasonably well. Simulation results show that the new particle filter is superior to the standard particle filter and the other filters such as the unscented particle filter (UPF), the extended kalman particle filter (PF-EKF), the EKF.

Key words: nonlinear system, iterated extended kalman filter, particle filter, the importance density function