Adaptive high-definition imaging

Adaptive high-definition imaging (HDI) is a data-adaptive approach to SAR image reconstruction based on superresolution techniques originally developed for passive sensor arrays. The problem at hand is the detection and recognition of ground-based targets in a clutter-dominated environment via UHF and L-Band foliage-penetrating SAR. Unfortunately, the resolution achieved in conventionally generated images is limited due to longer wavelengths and smaller bandwidths, as compared to high- resolution X- and Ka-Band SAR. A comparison of imaging techniques is presented, including conventional imaging, a 2D technique based on the MLM (Capon) algorithm, and a 2D version of the MUSIC algorithm. Results are presented for Wideband Rail SAR measurements of reflectors both in and out of foliage, demonstrating resolution improvement and clutter rejection. Also, results of processing data from an airborne wideband UHF SAR further demonstrate significant rejection of clutter which promises significant improvements in false-alarm performance.