High performance aerial maneuvering target tracking is one of the core tasks of modern early warning radar. To improve the tracking performance of the probability hypothesis density(PHD)filter

a new PHD track association algorithm based on measurement partition is proposed. The traditional PHD filter cannot give the individual trajectories of each target

and the filtering efficiency will decrease drastically when there are a large number of clutters in the tracking environment. Besides

the traditional methods also have measurement mismatch problem for adjacent multi-target tracking. To solve the above problems

the measurement set at each time step can be divided into existing targets

new targets and clutter measurement subsets by the sorting ellipsoidal gate method

which targets can be updated by corresponding measurement subsets

thus reducing redundant computing time and improving computational efficiency. Furthermore

the algorithm can adjust the weight of Gaussian component when targets are close to each other

by introducing a new weight distribution scheme

thus greatly reducing the state extraction error of adjacent targets and improving adjacent targets estimation accuracy. The simulation results show that the proposed method can improve the filtering efficiency and the adjacent targets tracking accuracy.