This paper deals with the ballistic target tracking problem using range spread measurements. Based on the fact that the range spread measurement distribution can be successfully approximated by the Gaussian mixture model, the problem is formulated in the framework of Gaussian sum filtering. A probabilistic mode merging algorithm is developed to ensure both the computational efficiency and the suboptimal performance. The proposed filter guarantees the operational reliability of the tracking algorithm, since it does not suffer from the degeneracy problem often encountered with the particle filter, which causes target tracking failure. The simulations for a typical ballistic target interception scenario demonstrate the effectiveness and the superior performance of the suggested method over the existing nonlinear filters.

This paper addresses the problem of re-entry target identification using radar cross section (RCS) measurements obtained from distributed radars. Considering that the RCS pattern is subordinate to the target class and the aspect angle, the target identification problem is formulated as maximum a posteriori estimation associated with the multiple hypothesis of these two unknowns. Once each hypothesis is propagated through the approximate aspect angle dynamics, the corresponding hypothesis probability is evaluated by using the available RCS measurements and the pre-trained RCS distribution for the corresponding hypothesis. To improve the target identification performance, the RCS measurement likelihood is calculated by exploiting the geometric relation between the target and the multi-radar. Computer simulations are carried out to demonstrate the superiority and reliability of the proposed method over the existing machine-learning based algorithm.