Research on Multi-Target Particle PHD Tracking Algorithm Based on Multi-Parameter Assistance

The traditional particle PHD filter only uses kinematic characters and parameters to measure,However,when the kinematic characters of targets are similar with each other (in distance,location,speed,acceleration,etc.) the weighted measurement will become more and more similar,so the tracks of targets will be closer and closer to each other.Eventually the tracks will mingle with each other and can not be distinguished,thus causing misjudgment and failing in achieving accurate tarcking.To solve such kind of target tracking problem,this paper proposes the particle PHD filter tracking algorithm based on multi-parameter assistance.That is,the target property information and parameters will be imported into the algorithm and the measurement difference is increased by calculating the combined likelihood value of the target property parameter and the kinetic character parameter,and then the combined likelihood value is used to update the particle PHD to filter miscellaneous waves and update multi-target State collection.The simulative analysis will enable the proposed algorithm to realize the short-distance multi-traget tracking and filtering in complex environment.