Abstract
Compressive sensing (CS)-based techniques can represent a very attractive approach to inverse scattering problems. In fact, if the unknown has a sparse representation and the measurements are properly organized, CS allows to considerably reduce the number of measurements and offers the possibility to achieve optimal (or nearly optimal) reconstruction performance. Unfortunately, the inverse scattering problem is nonlinear, while CS theory is well established only for linear recovery problems. As a contribution to overcome this issue, in this letter, we introduce two different CS-inspired approaches that exploit the “virtual experiments” framework, wherein it is possible to cast the inverse scattering problems in a linear form even in the case of nonweak targets.
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