Sparse reconstruction methods in RF Tomography for underground imaging

Abstract

Underground imaging involving RF Tomography is generally severely ill-posed posed. Tikhonov Regularization is perhaps the most common method to address this ill-posedness. The proposed methods are based upon the realistic assumptions that targets (e.g. tunnels) are sparse and clustered in the scene, and have known electrical properties. Therefore, we explore the use of alternative regularization strategies leveraging sparsity of the signal and its spatial gradient, while also imposing physically-derived amplitude constraints. By leveraging this prior knowledge, cleaner scene reconstructions are obtained.