Maneuvering Target Tracking Algorithm Based On Particle Filter

Maneuvering target tracking technologies have a widely used in the military field and civilian areas. In recent years, experts and scholars carried out in-depth research and achieved fruitful results. Because of the complexity tracking environment and the increasing mobility of the target, Target tracking system is put forward higher requirements to cope with the growing challenge. Therefore, looking for more adaptable algorithm for maneuvering target tracking is of great significance.This paper studies the maneuvering target tracking based on particle filter algorithm, some effective tracking algorithm have been proposed, to resolve the problem of complexity and low precision. Firstly, a new resample method based on artificial immune particle filter has been proposed, efficient optimization particle collection through rational screening mechanism to reduce the time in target tracking. Secondly, Tabu search particle filter algorithm has been proposed, new method improves the effectiveness of particles by dispersing partial optimum particles. Finally, proposed method is compared with the IMM algorithm to resolve the problem of glint noise interference in tracking close larger maneuvering target. The simulation results show that this algorithm is of the precision and real-time property, it has better estimated performance than IMM-PF.