Wide-Angle High Resolution SAR Imaging and Robust Automatic Target Recognition of Civilian Vehicles

This paper focuses on wide-angle synthetic aperture radar (SAR) imaging and automatic target recognition of civilian vehicles. A recently proposed hybrid data adaptive method is applied to generate accurate and sparse SAR images of civilian vehicles. We combine projection slice theorem (PST) with 2-D FFT to obtain a more accurate pose estimation than the established PST. Given the so-obtained pose estimates, the horizontal and vertical cumulative-sum-vector (CSV) profiles are utilized to focus the SAR image only on the vehicle of current interest. The corresponding vertical CSV is used as a simple feature for automatic target recognition (ATR). We adopt the local learning based feature selection for ATR. The effectiveness of the entire chain of imaging, pose estimation, feature extraction, and ATR methods is verified using the experimentation results based on the publicly available GOTCHA SAR data set. We demonstrate that the high resolution SAR imaging results in much improved ATR performance compared to the conventional SAR imaging.