Research On Multi-target Tarching Algorithm Based On Probability Hypothesis Density

Target tracking has an important research position in the field of high-tech military research and aeronautics and astronautics.As a research hotspot in the military and civil fields,multi-target tracking technology has a broad application prospect.With the development of research technology,high resolution radar detection technology has been applied in military and civil activities.In the process of extended target tracking,probability hypothesis density filtering algorithm overcomes the defect of data association and makes it a hot topic in multi-target tracking due to the increase of measurement data and the increase of computation complexity caused by data association.In recent years,the multi-target tracking method based on random sets has entered the vision of researchers,and it has given new vitality to the problem of multi-target tracking.This paper studies the multi-target tracking technology based on the random set theory and the probability hypothesis density filtering algorithm and the box particle filter algorithm.And it improves the algorithm in the resampling part of the algorithm,and optimizes the algorithm.The main contents of the study are as follows.First,this paper studies the multi-objective tracking method based on random set,the probability hypothesis density(PHD)algorithm and the box particle filter algorithm.The probability hypothesis density(PHD)filter based on box-particle(BP)technology can effectively improve the tracking accuracy of numbers of the multi-target tracking.And it also solved the tracking problem with unknown number of targets and missing targets.Then,point at the problems of lack of tracking accuracy and missing tracking targets in the BP-PHD algorithm,this paper presents some detailed measures about improving tracking.Based on the particle filter theory,we found that in the process of resampling,the particle size distribution can not be optimized.So,in this paper,we replaced the traditional random sub sampling resampling method with the partition resampling method to improve the method of box-particle filter.Then,we simulate and experiment the improved algorithm combined to PHD filtering by using MATLAB.The experimental results show that the improved method of tracking multi-target with the box-particle PHD filter is more accurate than the original algorithm and the OSPA distance is smaller,so the effect of the improved tracking method is better.