Resolvable cluster target tracking based on wavelet coefficients and JPDA

Abstract

It is difficult to track the cluster target accurately because of the interaction between the clusters. In this paper, the tracking problem of cluster targets is studied. Firstly, the stochastic differential equation is used to model the cooperative motion of clusters, and the movement and measurement model of cluster target is derived. Secondly, the cluster tracking filter in the framework of Bayesian filtering is deduced, and a method of cluster partition using wavelet coefficients is proposed. Finally, in order to achieve measurement correlation in clutter environment, the JPDA target tracking algorithm is derived. The simulation results show the effectiveness of the proposed cluster target tracking algorithm in clutter environment.