An Intelligent System for High Resolution Range Radar Signature Identification

The goal of this project is to develop a neural-network based system for air-to-air aircraft identification from High Resolution Range (HRR) radar target return signatures. Training and testing of this system will be carried out using appropriate data sets from the large data base of HRR-radar-acquired signatures of known targets residing at the Wright Patterson Air Force Base. In performing target identification, the system will optimally nonlinearly map the observed target signature x into a low-dimensional feature vector z, and then classify z according to an appropriate statistical decision rule like Neyman-Pearson's. For this purpose, the statistics of z will be acquired from the training data by nonparametric methods. In order to increase robustness and accuracy, the system will be implemented in the form of a hierarchical classifier. In such an implementation, provision will be made for bringing information from other sources to reduce uncertainty through data fusion. Preliminary results on a limited data set look encouraging.