Preprocessing the reciprocity gap sampling method in buried-object imaging experiments

A reciprocity gap linear sampling method (RG-LSM) coupled with an analytic continuation method is proposed to localize and retrieve the shape of objects buried under a rough surface from multistatic data at a fixed frequency. The obtained procedure makes feasible the application of the RG-LSM algorithm to imaging experiments where the data are collected in the upper domain. It does not require the computation of the Green's function of the background layered medium and also does not require any a priori knowledge on the number or the physical properties of the buried scatterers. The efficiency and robustness of the method are validated through various numerical experiments for single and multiconnected objects.