Survey of Particle Filter Target Tracking Algorithms

ZAN Meng’en, ZHOU Hang, HAN Dan, YANG Gang, XU Guoliang

1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
• Online:2019-03-01 Published:2019-03-06

粒子滤波目标跟踪算法综述

1. 北京交通大学 电子信息工程学院，北京 100044

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.