An Adaptive Particle Filter for Target Tracking Based on Double Space-Resampling

Particle filter has been widely applied in nonlinear target tracking due to the ability carry multiple hypothesis and relaxation of linearity/Gaussian assumption. In this paper, an adaptive double space-resampling particle is proposed increase efficiency robustness filtering by adjusting sample size. The first resampling operation, adopted before prediction samples, generates a larger number equal-weighted samples some auxiliary enhance filtering. second resampling, between updating step, decreases size for weight which most time consumption part filter. space sampling technique used both space-resampling, adjusts according not only weights but also their spatial distribution. improved algorithm enhanced, simultaneously. degeneracy impoverishment problems can be counteracted. Simulation experiment contrast results demonstrate that method robust efficient.