Research on Particle Filter with Adaptive Resampling Based on Diversity Measure
Jingmin Xu · 2012
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.