SBIR-STTR Award

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.

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.

Keywords:
Saliency Technology, Compact Features, Uncertainty Propagation, Robust Cid