Feature Enhanced Imaging with Compressed Fractional SARSensors: Inverse Problem Formalism and l2–l1 StructuredDescriptive 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 L1 metric structured TV regularization into the L2 metric structured DEDR data agreement objective function and solves the overall reconstructive imaging inverse problem employing the POCD-DEDR-TV-restructured MVDR strategy.