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In this paper, the full-vectorial three-dimensional ( 3D ) microwave imaging ( MI ) of sparse scatterers is dealt with. Towards this end, the inverse scattering ( IS ) problem is formulated within the contrast source inversion ( CSI ) framework and it is aimed at retrieving the sparsest and most probable distribution of the contrast source within the imaged volume. A customized multi-task Bayesian compressive sensing ( MT-BCS ) method is used to yield regularized solutions of the 3D-IS problem with a remarkable computational efficiency. Selected numerical results on representative benchmarks are presented and discussed to assess the effectiveness and the reliability of the proposed MT-BCS strategy in comparison with other competitive state-of-the-art approaches, as well.

Full-Vectorial 3D Microwave Imaging of Sparse Scatterers through a Multi-Task Bayesian Compressive Sensing Approach