关键词: 粒子滤波, 目标跟踪, 重采样, 高斯-邻域搜索, 重要序列采样

Abstract: In order to solve nonlinear global optimization problems of particle filter, on the basic of small weighted particles are removed and the number high weighted particles are increased by re-sampling, Particle Filter of Neighborhood Iteration Re-sampling（NIRPF） is proposed for tracking. Firstly, particles are predicted and Sequential Importance Sampling（SIS） is used for particle empowering value. Then, in the process of searching a high probability of posterior probability density, the position of single particle is updated. Gaussian weighted neighborhood search is adopted for weighting all the particles iteratively. Finally, the current status is estimated. Bearings target tracking problem involves two static observations and two types of targets non-motorized and motorized. The effectiveness of proposed method is verified by Monte Carlo simulation results. The proposed method has a faster initial convergence speed in comparison with square-root cubature Kalman filter, cubature particle filter and random search-particle filter, and the evaluation of Root Mean Square Error（RMSE） and Root Time Averaged Mean Square（RTAMS） on non-maneuvering target and maneuvering is much better.

Key words: particle filter, target tracking, re-sampling, Gaussian weighted neighborhood search, Sequential Importance Sampling（SIS）