  An innovative inverse scattering (IS) method is proposed for the quantitative
imaging of pixel-sparse scatterers buried within a lossy half-space. On the one
hand, such an approach leverages on the wide-band nature of ground penetrating
radar (GPR) data by jointly processing the multi-frequency (MF) spectral
components of the collected radargrams. On the other hand, it enforces sparsity
priors on the problem unknowns to yield regularized solutions of the fully
non-linear scattering equations. Towards this end, a multi-task Bayesian
Compressive Sensing (MT-BCS) methodology is adopted and suitably customized to
take full advantage of the available frequency diversity and of the a-priori
information on the class of imaged targets. Representative results are reported
to assess the proposed MF-MT-BCS strategy also in comparison with competitive
state-of-the-art alternatives.
