This paper proposes a practical algorithm to identify re-entry targets using radar cross-section (RCS) measurements. Considering that the RCS pattern depends on two unknowns, the target shape and aspect angle, we formulate the target identification pr...
This paper proposes a practical algorithm to identify re-entry targets using radar cross-section (RCS) measurements. Considering that the RCS pattern depends on two unknowns, the target shape and aspect angle, we formulate the target identification problem as a posteriori probability estimation of the corresponding hypothesis. Each hypothesis is propagated by considering the aspect angle variations, and its probability is calculated by using a pre-trained RCS distribution model and the measurement sequence. By merging hypotheses with the same angular transition history, we develop a suboptimal algorithm suitable for real-time implementation. Simulation and analysis results for a typical re-entry target tracking scenario are presented to validate the superiority and reliability of the proposed method over existing HMM-based approaches.