New Radar Exploitation Methods for Combat Identification

ABSTRACT: The Phase II program will develop and mature a new salient physics-driven solution for CID feature design and classification to support onboard CID and decision fusion for remotely piloted vehicles. Saliency analysis will be used to develop databases of compact, simple geometric features derived from known, discriminative, and robust scattering physics. A new Bayesian probabilistic classifier be developed and validated for SAR and HRR modes that exploit a hybrid combination of conventional sparse, data-driven signature features and efficient physics-based features. Uncertainty will be propagated from feature extraction to decision. Target libraries will include both civilian and military vehicles. The classifier will be developed in a hierarchical fashion that utilizes as little target information as possible in order to achieve CID objectives. Primarily, efficient geometric features will be used for most CID tasks. When increased CID fidelity or reliability are required (e.g., high value targets, or known confusors), sparse-signature salient feature exploitation techniques will be used to supplement the classifier with additional information. The salient physics-driven solution will address the limited processing and memory challenges associated with onboard CID accuracy and confidence. Furthermore, salient physics-based feature design is an enabling technology for non-cooperative target modeling. BENEFIT: The Phase II program is designed to address the efficiency and sustainability issues associated with the development, operation, and maintenance of current non-cooperative ATR technology. The products of the proposed program will ultimately lead to a low-cost, quick turn-around solution for target insertion into CID 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 CID. The databases of compact feature sets identified by saliency analysis will provide CID accuracy and reliability. The proposed program represents a significant change in CID design practices; tasking under the Phase I resulted in a proof of concept that addresses the system requirements of and offers risk reduction to anticipated future AFRL requirements. In Phase II, SIG will further develop the technical maturity of this architecture, explore algorithm migration paths and requirements to operational use, and demonstrate performance in a lab environment.