High Range Resolution (HRR)-Surrogate SAR Target Identification

ABSTRACT: 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.