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In this paper, the retrieval of sparse defects within known two-dimensional structures is addressed. A Bayesian Compressive Sensing technique has been developed to determine the differential induced currents thanks to a proper definition of an inhomogeneous Green's function enabled from the a-priori knowledge of the unperturbed structure. Successively, a contrast source inversion method has been applied...
The solution of linear microwave imaging problems is considered in this work through innovative classes of Compressive Sensing (CS) methods. More in detail, the formulation of the inversion process in transformed domains with sparseness-regularized formulations is considered. Representative numerical examples illustrating the potentialities and limitations of the arising CS inversion approaches are...
This paper presents an innovative microwave imaging technique for detecting defects in dielectric or conductive media developed through the Bayesian Compressive Sensing theory. Thanks to the a-priori knowledge of the medium unaffected by defects, the inversion problem is formulated as the estimation of the sparse differential equivalent currents within the support of the defects. The strategy has...
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