SBIR-STTR Award

The Air Force has invested considerable resources into collecting and synthesizing radar data for training and testing ATC/R systems. Significant cost savings may be realized if these existing datasets may be leveraged for training new sensors and modalities. Signal Innovations Group offers a new paradigm for automatically identifying statistically salient features for ATR systems from combined sources of existing surrogate and limited operational data. This approach departs from conventional techniques that attempt to compensate for numerous sources of degradation, through pre-processing, noise estimation, modeling and manual intervention in order to obtain perfect HRR template matches. Saliency analysis identifies sparse subsets of features (e.g. HRR range bins) that are both statistically significant for ATR and robustly manifested in data. Salient features have been shown to provide superior classification performance compared to full-dimensional HRRs. Additionally, SIG proposes migrating away from conventional HRRs to simple, compact sets of physics-based features, derived from EM phenomenology, which may be extracted independently from both SAR and MTI. This paradigm avoids reliance on complex pre-processing to compensate for distortion by utilizing statistical inference techniques to identify robust phenomenology. Adverse phenomena (e.g. multi-bounce or shadowing) are highly variable and will be rejected by the saliency analysis.

Benefit:
The successful program will result in a capability to automatically leverage data across multiple sensors and modalities for ATR development. A common database of physics-based features will be developed across families of sensors. This will reduce the costs of training new ATR systems and decrease the time required to deploy new capabilities. The Bayesian framework naturally supports the fusion of information from alternative sensing modalities such as optical, infrared, or hyper-spectral. The physics-based features in the radar regime may be combined with corresponding physics-based features in these alternate regimes, through higher-level Bayesian processes, to improve overall ATR performance. The sequential Bayesian inference framework for identifying salient features has potential extensions in both military and commercial applications. This framework may be applied to train new ATR systems, with very limited characterized data, using surrogate datasets from existing sensors. This applies to new sensors, including EO and IR, developed for military or geospatial applications as well as for medical imaging and diagnostic applications. For example, this framework may be applied towards improved tissue characterization and disease detection with medical imaging systems, automated facial recognition systems, genetic/protein structure and function determination for bioinformatics analysis, and next-generation internet search engine development.

Keywords:
Physics-Based Salient Features, Bayesian Inference, Uncertainty Propagation

This program will develop salient feature analysis and saliency cueing capabilities for SAR-based exploitation and demonstrate this technology with operationally relevant data and classifiers. This saliency technology will immediately enable the development of exploitable, robust, compact feature databases for sustainable radar CID. This new SAR-based saliency capability will complement existing HRR-based saliency technology by providing either independent or joint discovery of sparse sets of robust, exploitable features across these two common radar sensing modalities, in support of air-to-air or air-to-ground moving and stationary CID missions. This framework provides a balance between a compact target representation and target generalization across in-class variations. The underlying probabilistic model captures the uncertainty inherent to the training process due to limited or noisy data, and propagates these probabilities in a mathematically rigorous manner to downstream processes. The proposed work for this program will focus on near-term capabilities of sustainable radar CID database development and will lay the fundamental groundwork for potential fusion of radar and EO features based upon saliency and target geometry.

Benefit:
Phase II will result in a combined salient feature analysis, saliency cueing, and classification performance analysis capability for both HRR and SAR radar modalities integrated into POSSIBLE. This capability will enable the automatic identification of compact exploitable radar signature features over fewer target aspect states for more efficient and sustainable CID databases. While the salient feature analysis framework will be leveraged on the proposed program for classification with radar sensor modalities, the framework easily generalizes to other data modalities and exploitation applications, such as detection, recognition, identification, and general data categorization. Medical device manufacturers are developing cutting-edge sensors and equipment that is leading to revolutionary advances in non-invasive diagnosis of a variety of diseases, especially cancer. These devices have the ability to gather a significant amount of data, much more than a physician or technician can handle alone. Additionally, commercial satellite imagery providers are developing sensors that collect extremely high-resolution imagery. While more pixels generally provide more information, large amounts of data also lead to increased burden on image analysis, storage, and query. Therefore, automated processing, including rigorous understanding of salient features, is necessary for realizing the full potential of these new medical devices and satellite imagery. SIG is actively engaged with sensor and data providers in each of these industries and will leverage these relationships to commercialize the products of this Phase II SBIR.

Keywords:
radar exploitation, saliency technology, compact features, uncertainty propagation