Traditional radar target tracking algorithms usually cause more false alarms under complex environment. It is useless for single tracking or detecting algorithm to filter clutters effectively. Aiming at this problem

a new learning pattern combined attribution-detector and PN-learning framework is proposed to improve performance of joint detection and tracking framework which is based on PHD algorithm. The PN module is used to extract high-confidence targets from the results of attribution detector. Then these filtered results is used to retrain detector and modify outputs of PHD. Simulation tests show that the new method PN—PHD not only effectively improve target tracking capability in strong clutter environment

but also provide tracking information which original PHD method cannot offer.