迭代测量更新CDKF的粒子滤波方法

为了提高中心差分卡尔曼粒子滤波（CDKFPF）算法跟踪时的估计精度，提出了一种基于迭代测量更新CDKF的粒子滤波（ICDKFPF）新算法。该算法利用迭代中心差分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，并用Levenberg-Marquardt方法对状态协方差进行修正，使粒子的观测信息得到充分有效的利用，更加符合真实状态的后验概率分布。仿真结果表明，所提出算法的估计性能要明显优于标准的粒子滤波（PF）和中心差分卡尔曼粒子滤波（CDKFPF）。

Iterated central difference Kalman particle filter

In order to improve tracking estimation accuracy of existing central difference Kalman particle filter（CDKFPF）,a new particle filter algorithm based on iterative measurement update CDKF is proposed.The algorithm produces the important density function of particle filter using maximum posteriori estimate of iterative central difference Kalman filter,and amends the state covariance using Levenberg-Marquardt method,so that the particle’s observed information is effectively used,and this is more in line with the true state of the posterior probability distribution.The simulation results show that estimation performance of the proposed algorithm is much better than the standard particle filte（rPF）and central difference particle filte（rCDKFPF）.