%0 Journal Article
%T Particle Filter Based on Adaptive Part Resampling<br>自适应不完全重采样粒子滤波器
%A ZUO Jun-Yi
%A ZHANG Yi-Zhe
%A LIANG Yan
%A <br>左军毅
%A 张怡哲
%A 梁彦
%J 自动化学报
%D 2012
%I 
%X A particle filter based on adaptive part resampling (APRPF) is proposed to solve the problem of particle impoverishment introduced by traditional resampling algorithm. In APRPF, only small portion of samples are resampled in a step by step manner, and a recursive formula is developed to evaluate the measurement of particle degeneracy (MPD). The resampling process continues until MPD satisfies the given condition. After resampling, the particle set consists of two subsets, one contains new born particles, and another contains unresampled particles. The former can help to alleviate particle degeneracy, whereas the latter is in favor of improving the diversity of particles. Experimental results show that APRPF has less computational cost and more precise filtering results than sampling importance resampling (SIR), auxiliary particle filter (APF) and regularized particle filter (RPF).
%K Particle filter (PF)
%K sample degeneracy
%K sample impoverishment
%K part resampling<br>粒子滤波
%K 粒子退化
%K 粒子贫化
%K 不完全重采样
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=28538CA98C3000782461C19B2129302C&yid=99E9153A83D4CB11&vid=16D8618C6164A3ED&iid=E158A972A605785F&sid=AEE2F90BC0DB5F35&eid=4A8412AEEC89236B&journal_id=0254-4156&journal_name=自动化学报&referenced_num=1&reference_num=10