Feature Enhanced Imaging with Compressed Fractional SAR Sensors: Inverse Problem Formalism and l(2)-l(1) Structured Descriptive Regularization Framework

We address a new technique for feature-enhanced radar imaging with compressed/fractional SAR data that unifies the descriptive experiment design regularization (DEDR) framework with the total variation (TV) image enhancement paradigm and the sparsity preserving regularizing projections onto convex solution sets (POCS). The new framework incorporates the l(1) metric structured TV regularization into the l(2) metric structured DEDR data agreement objective function and solves the overall reconstructive imaging inverse problem employing the POCD-DEDR-TV-restructured MVDR strategy.