Abstract
Particle filtering shows great promise in addressing a wide variety of non-linear and /or non-Gaussian problem. A crucial issue in particle filtering is the selection of the importance proposal distribution. In this paper, the iterated extended Kalman filter (IEKF) is used to generate the proposal distribution. The proposal distribution integrates the latest measurements into system state transition density, so it can match the posteriori density well. The simulation results show that the new particle filter superiors 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.
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