精确测量系统的聚类准蒙特卡罗粒子滤波算法  

A Cluster-based Quasi-Monte Carlo Particle Filter for Accurate Measurement System

作　　者：赵玲玲[1] 马培军[1] 苏小红[1] 

机构地区：[1]哈尔滨工业大学计算机科学与技术学院

出　　处：《信号处理》2014年第2期127-134,共8页Journal of Signal Processing

基　　金：国家自然科学基金项目(61175027);国家青年科学基金项目(61305013);中央高校基本科研业务费专项资金(HIT.NSRIF.2014071)资助

摘　　要：针对粒子滤波在精确测量系统中由于似然分布过于尖锐引起的失效问题,提出一种基于确定性准蒙特卡罗采样和聚类方法的粒子滤波算法,当判定重要性样本缺失时,利用聚类方法提取提议分布中的关键样本,在由关键样本构成的新的支撑集基础上,确定采样空间和繁殖样本个数,并采用准蒙特卡罗方法生成新的确定性样本序列,以获取有效样本,避免失效现象的发生。仿真实验表明,该算法在测量噪声较小的情况下,与一般粒子滤波算法相比,可以获得更为精确和稳定的状态估计。Aiming at the particle filter＇ s inefficacy problem caused by narrow likelihood of accurate measurement system,a new particle filter with resampling step based on clustering and determination quasi-Monte Carlo is proposed.When importance samples are lost,the algorithm extracts key samples using clustering method,and then determines the number of spring-offs of them and sampling space according to the new support set composed of key samples.New particles are generated by QMC,in order to obtain more efficient samples and avoid the inefficacy phenomena.Simulations show that,compared to the general resampling step of particle filter,this algorithm obtains more accurate and steady state estimation.

关 键 词：粒子滤波 准蒙特卡罗采样 聚类 精确测量 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]