迭代扩展卡尔曼粒子滤波器

Iterated extended kalman particle filtering

提出了一种基于迭代扩展卡尔曼的粒子滤波新方法.该方法利用迭代扩展卡尔曼滤波的最大后验概率估计产生粒子滤波的重要性密度函数,使重要性密度函数能够融入最新观测信息的同时,更加符合真实状态的后验概率分布.仿真结果表明,提出的迭代扩展卡尔曼粒子滤波的估计性能要明显优于标准的粒子滤波、扩展卡尔曼粒子滤波和unscented粒子滤波.

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.