Multilevel descriptive experiment design regularization framework for sparsity preserving enhancement of radar imagery in harsh sensing environments

Abstract

We address a new approach to a reconstructive imaging inverse problems solution as required for enhancement of low resolution real aperture radar/fractional SAR imagery in harsh sensing environments. To preserve the image and image gradient map sparsity peculiar for real-world remote sensing (RS) scenarios, we aggregate the minimum risk inspired descriptive experiment design regularization (DEDR) framework for balanced image resolution enhancement over noise suppression with two additional regularization levels: (i) the variational analysis inspired minimization of the image total variation (TV) map and (ii) the sparsity preserving regularizing projections onto convex solution sets (POCS). The new framework incorporates the TV metric structured regularization into the weighted 2 A metric structured DEDR data agreement objective function and suggests the solver for the overall reconstructive imaging inverse problem employing the DEDR-TV-POCSrestructured MVDR strategy. The DEDR-TV-POCS method implemented in an implicit iterative fashion outperforms the competing nonparametric adaptive radar imaging techniques both in the resolution enhancement and computational complexity reduction as verified in the reported simulations. Index Terms—Descriptive experiment design regularization, fractional synthetic aperture radar (F-SAR), image enhancement, remote sensing, total variation.