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An innovative inverse scattering (IS) method is proposed for the quantitativeimaging of pixel-sparse scatterers buried within a lossy half-space. On the onehand, such an approach leverages on the wide-band nature of ground penetratingradar (GPR) data by jointly processing the multi-frequency (MF) spectralcomponents of the collected radargrams. On the other hand, it enforces sparsitypriors on the problem unknowns to yield regularized solutions of the fullynon-linear scattering equations. Towards this end, a multi-task BayesianCompressive Sensing (MT-BCS) methodology is adopted and suitably customized totake full advantage of the available frequency diversity and of the a-prioriinformation on the class of imaged targets. Representative results are reportedto assess the proposed MF-MT-BCS strategy also in comparison with competitivestate-of-the-art alternatives.

Multi-Frequency GPR Microwave Imaging of Sparse Targets Through a Multi-Task Bayesian Compressive Sensing Approach