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

Abstract

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