Maritime Target Automatic Target Recognition from Inverse Synthetic Aperture Radar (ISAR) Utilizing Machine Learning

Radio frequency missile seekers operating over the ocean against maritime targets must identify specific ship classes on a tight timeline while addressing the inherent complexity of low altitude over-ocean RF propagation and 6-DOF ship motion that occurs during the coherent processing interval of a range-Doppler image. Our Phase 1 program will develop a practical machine-learning based ISAR ATR system that addresses these challenges. The system will be designed to achieve high target recognition and confuser rejection performance, to be robust to target state and environmental conditions, to be easily extensible to classification of new targets, and so that online processing may be implemented on low SWAP hardware to meet demanding smart munitions timelines. To create this design, the Phase 1 effort will integrate and utilize high fidelity RF propagation and scattering simulations to drive trade study analyses that will identify deep learning architecture attributes, model training strategies, and sensing and signal processing approaches that address seeker-based ISAR target recognition challenges in maritime environments.