%0 Journal Article
%T 交互式多模型粒子滤波优化重采样算法<br>Interacting multiple model particle filter optimization resampling algorithm
%A 周卫东
%A 孙天
%A 储敏
%A 崔艳青
%J 北京航空航天大学学报
%D 2017
%R 10.13700/j.bh.1001-5965.2016.0348
%X 摘要 针对标准交互式多模型粒子滤波(IMMPF)算法中存在粒子退化及多样性匮乏问题，提出了交互式多模型粒子滤波优化重采样(IMMPFOR)算法，利用线性优化理论改善模型中具有小权值的粒子精度。该算法的新颖性体现在给定量测信息条件下，利用线性优化方法及模型交互概率将每个模型中拥有小权值的粒子替换成新的粒子。新的粒子既包含本模型中粒子信息，又包含了本模型与其他模型交互后的粒子信息。目标跟踪的仿真结果证明：每个模型新产生的粒子集合可以准确地近似真实状态后验概率密度函数，系统的估计精度与标准IMMPF算法相比有较大提升。<br>Abstract：For the problem of particles degeneration and lack of diversity in standard interacting multiple model particle filter (IMMPF) algorithm, a novel algorithm is presented, which is referred to as interacting multiple model particle filter optimization resampling (IMMPFOR) algorithm using linear optimization method in each model to improve the small-weight particles. The novelty of this algorithm lies in replacing the small-weight particles with new particles after the measurement information is received. New particles contain not only the information of the particles in the current model, but also the information of the particles in interacting models. The tracking simulation results show that the posterior probability density function of each model with newly generated set of particles accurately approximates the real state posterior probability density function, and the estimation accuracy of IMMPFOR is higher than the standard IMMPF algorithm.
%K 交互式多模型
%K 粒子滤波
%K 线性优化
%K 重采样
%K 粒子退化&lt
%K br&gt
%K interacting multiple models
%K particle filter
%K linear optimization
%K resampling
%K particle degeneration
%U http://bhxb.buaa.edu.cn/CN/abstract/abstract14288.shtml