关键词: 目标跟踪, 粒子滤波, 重采样, 重要性采样, 特征融合, 自适应粒子滤波

Abstract:

With the development of artificial intelligence science, target tracking has become a hotspot for domestic and foreign scholars. In recent years, many target tracking algorithms have been proposed. Among them, the classical Kalman filtering algorithm is often used in the target tracking field. However, in the actual situation, the target tracking process often involves nonlinear non-Gaussian problems. As the particle filtering algorithm has better performance in non-Gaussian nonlinear systems, it is introduced into the field of target tracking research. In view of the problems of poor tracking accuracy and low real-time performance of particle filtering algorithm, many domestic and foreign scholars have proposed many improved methods. In this paper, the basic ideas of related improved methods are introduced from three aspects：feature fusion, algorithm fusion and adaptive particle filtering. The development direction of particle filtering algorithm in target tracking field is prospected.

Key words: target tracking, particle filtering algorithm, resampling, importance sampling, feature fusion, adaptive particle filter