AN ADAPTIVE RESAMPLING STRATEGY IN PARTICLE FILTER
Particle filter has been widely applied into many fields in recent years.Combined with the deficiency analysis of particle filter,an adaptive resampling strategy based on diversity guidance is proposed.Firstly,the adaptive resampling step in particle filter is tuned based on two diversity measures which are effective sample size and population diversity factor.Moreover,the operation of particle mutation after resampling is integrated into PF so as to assure the diversity of particle sets.Then,an optimized resampling strategy in PF is presented.It drew from the advantage that resampling is done faster in partial stratified resampling algorithm.At the same time,aimed at the disadvantage of PSR algorithm in PF,it used the weights optimal idea to improve the performance of PF.With the simulation program using matlab 7.0 to track a single target motion from a fixed visual observation points,the validity of the proposed method is verified.
Particle filter Adaptive resampling Diversity measure Particle mutation Partial stratified resampling Weight optimal idea
JIN-XIA YU YONG-LI TANG XIAN-CHA CHEN QIAN ZHAO
College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, China State Grid Eleetrie Power of HeNan Ji-Yuan Power Supply Company, Jiyuan 454650, China
国际会议
2011 International Conference on Wavelet Analysis and Pattern Recognition(2011小波分析与模式识别国际会议)
桂林
英文
144-149
2011-07-10（万方平台首次上网日期，不代表论文的发表时间）