Knowledge-based multiple hypothesis tracking and identification of manoeuvring reentry targets

This paper addresses the integrated tracking and identification problem of a manoeuvring reentry target that performs intentional lateral manoeuvres to disrupt ground radars. Unlike previous approaches, prior knowledge of the lift-induced drag is incorporated into a new manoeuvring model to describe the reentry target dynamics more explicitly. This model can account for the constraint between lift and drag, which is beneficial in ensuring the reliability of target state estimation. Noticing that the lift-induced drag is an inherent characteristics of a reentry target that distinguishes the target's identity from others belonging to the same class, the integrated target tracking and identification problem is formulated within the framework of the multiple hypothesis testing about a set of manoeuvring models constructed by different prior knowledge. The proposed approach enables the authors to derive the optimal solution to the given problem in a mathematically rigorous manner. To cope with the real-time implementation issue, a hypothesis merging strategy is also devised in view of maintaining the target identification performance. Simulation results demonstrate that the proposed scheme provides superior performance and reliability both in target tracking and identification compared to the existing method, despite imperfectness of prior knowledge.