The Iterated Extended Kalman Particle Filter Li Liang-qun, Ji Hong-bing,Luo Jun-hui School of Electronic Engineering, Xidian University ,Xi’an 710071, China Email:
[email protected] Abstract— Particle filtering shows great promise in addressing Simulation results are given in Section 5.Conclusions are a wide variety of non-linear and /or non-Gaussian problem. A presented in Section 6. crucial issue in particle filtering is the selection of the importance proposal distribution. In this paper, the iterated extended 2. PARTICLE FILTER kalman filter (IEKF) is used to generate the proposal distribution. The proposal distribution integrates the latest 2.1 Modeling Assumption observation into system state transition density, so it can match Consider the nonlinear discrete time dynamic system: the posteriori density well. The simulation results show that the new particle filter superiors to the standard particle filter and xkk= fx( k−1,vk−1) (1) the other filters such as the unscented particle filter (UPF), the extended kalman particle filter (PF -EKF), the EKF. zhkk= ( xk,ek) (2) where f :ℜnnxv×ℜ →ℜnxand h :ℜ×nnxeℜ→ℜnz represent the 1. INTRODUCTION k k system evolution function and the measurement model Nonlinear filtering problems arise in many fields n n function. x is the system state at time , z is including statistical signal processing, economics, statistics, xk ∈ℜ k zk ∈ℜ biostatistics, and engineering such as communications, radar n m the measurement vector at time k . vk −1 ∈ Rand eRk ∈ tracking, sonar ranging, target tracking, and satellite represent the process noise and the measurement noise navigation [1-7].