基于集合卡尔曼滤波的改进粒子滤波算法

提出一种基于集合卡尔曼滤波的粒子滤波改进方法。该方法利用集合卡尔曼滤波的最大后验概率估计产生粒子滤波每一时刻各粒子的建议分布函数，使建议分布函数融入最新观测信息的同时，更加符合状态的真实后验概率分布。该方法在对粒子滤波的建议分布进行估计时使用采样方法近似非线性分布，且采样点数灵活可变，使计算精度和算法效率得到提高。仿真结果表明，提出的集合卡尔曼粒子滤波的估计性能明显优于标准粒子滤波、扩展卡尔曼粒子滤波和无迹粒子滤波。

Improved particle filter based on ensemble Kalman filter

An improved particle filter based on the ensemble Kalman filter(EnKF) is described.The EnKF is used to propagate the particle's proposal distribution of particle filter in every time step.Because the EnKF can attain a maximum posterior estimation of the nonlinear system,and the proposal distribution function integrates the latest observation into system state transition density,so the proposal distribution can approximate the true posterior distribution more accurately.This new algorithm uses sample ensembl...