基于多样性向导的自适应重采样粒子滤波研究  

Research on Particle Filter with Adaptive Resampling Based on Diversity Measure

作　　者：于金霞[1] 汤永利[1] 许景民[1] 

机构地区：[1]河南理工大学计算机科学与技术学院,焦作454003

出　　处：《计算机科学》2012年第6期231-234,共4页Computer Science

基　　金：河南省高校科技创新人才支持计划(2009HASTIT021);河南省高等学校青年骨干教师资助计划(2010GGJS-059);河南理工大学青年骨干教师基金资助

摘　　要：由于在非线性非高斯系统和多模处理能力上的优越性,粒子滤波算法已经被广泛应用。针对粒子滤波算法现有缺陷分析,提出一种基于多样性向导的自适应重采样粒子滤波。首先,基于多样性向导自适应调整重采样阈值。在基于有效样本大小的自适应重采样技术之上,借助了另一多样性测度即种群多样性因子来自适应地调整有效样本大小的阈值;而且,在重采样之后引入样本变异操作来确保样本的多样性。然后,提出了一种改进的部分分层重采样算法。该算法借鉴部分分层重采样执行快、时间短的优点,同时结合权重优化的思想改进重采样的样本权重计算。最后,通过仿真实验验证了所提粒子滤波算法的性能和有效性。As its advantage in non-linear non-Gaussian system and multi-mode processing,particle filter（PF） has widely been applied into many fields in recent years.With the deficiency analysis of existing algorithm,a particle filter with adaptive resampling based on diversity guidance was presented.Firstly,it adaptively tuned the resampling threshold by diversity guidance.Based on the adaptive resampling techniques on effective sample size,other diversity measure,population factor,was used to adjust the resampling threshold.Moreover,the operation of particle mutation after resampling was integrated into PF so as to assure the diversity of particle sets.Then,an improved partial stratified resampling（PSR） in PF was proposed.It drew from the advantage of PSR in implementation speed and time.In addition,it combined with the weights optimal idea to improve the performance of PF.With the simulation experiments,the validity of the proposed method was verified.

关 键 词：粒子滤波 自适应重采样 多样性 改进的部分分层重采样 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]