Exploiting compressive sensing for non linear inverse scattering

Compressive Sensing is a powerful paradigm, which has recently emerged as a way to recover 'sparse' signals, i.e. signals where it is known that only a few coefficients of a given representation (whose indices are not known) are different from zero. Provided given conditions are fulfilled amongst the original cardinality of the unknown signal, the number of elements different from zero, and the number of independent measurements, CS theory provides theoretical results and numerical procedures such to guarantee a faithful recovery of the unknown signal.