New Radar Exploitation Methods for Combat Identification

ABSTRACT: Current automatic target recognition (ATR) training processes require expensive data collections or extensive, high fidelity target modeling and validation whose costs and lead times will limit the ongoing sustainability of ATR target databases. Radar based systems for combat identification (CID) suffer from sustainability issues due to the extreme complexity of the target databases and the high costs and latency of incorporating new targets to meet evolving mission needs. In order to enable sustainable, reliable radar CID through salient physical features, SIG proposes to leverage existing HRR-based saliency technology to develop a knowledge base of target class and aspect dependent geometric features (such as the distance between critical scattering structures such as a bumper and windshield) from existing data for compact, robust CID. This simple and robust physical feature domain will be used to train a novel probabilistic classifier architecture that characterizes the uncertainty of target decisions. SIG will emphasize the selection of physics-based features that are relevant across a wide range of sensing modalities (HRR, SAR, EO), expanding the availability of target training data and facilitating future development and capabilities. These salient features enable sustainable development, operation, and maintenance of a compact, robust, and discriminative CID database. BENEFIT: A successful Phase I will result in a CID ATR framework that addresses the efficiency and sustainability issues associated with the development, operation and maintenance of current non-cooperative ATR technology. The proposed method provides a low-cost, quick turn-around solution for target insertion into ATR databases, at a significant savings compared to conventional signature database enablers. The selection of salient, physics-based features will reduce the template/database dimensionality for multi-phenomenology ATR by replacing image/signature template databases with compact feature sets. The proposed Phase I results in a proof of concept that addresses the system requirements of and offers risk reduction to future AFRL efforts.