Signal Processing and Exploitation for High-Dimensional Synthetic Aperture Radar (SAR)

A probabilistic ATR framework is proposed to exploit coincident multi-aspect radar and EO video data for target detection, tracking, and classification/identification. The mathematical framework is constituted by four principal components with a particular focus on exploitation of 3D information from radar scattering: 1) extraction of features indicative of shape and structure from radar waveforms, 2) tracking of targets from coincident EO and radar data to estimate target-sensor pose, 3) estimation of 3D target representations from radar data, and 4) development of pattern recognition algorithms to provide target classification and identification. This framework will exploit all sources of information in the EO/radar data. The probabilistic framework supports rigorous characterization of the uncertainty associated with each component as well as propagation to subsequent dependent components. This uncertainty is ultimately captured in the target classification and identification results. The products from the proposed research offer the potential for significant further improvements in target ID performance for realistic operational environments. SIG will collaborate with AFRL to identify the appropriate measured data sets to demonstrate and evaluate the framework. Potential data sets include the AFRL Layered Sensing collect (Angel Fire EO with GOTCHA SAR) and/or Bluegrass data (Constant Hawk EO with JSTARS GMTI). BENEFIT: Layered sensing and multi-sensor fusion, medical imaging, traffic analysis, multi-sensor security, next-generation ISR