Subsurface Radar Imaging by Optimizing Sensor Locations in Spatio-Spectral Domains.

This article deals with subsurface radar imaging (SRI) for a 2-D scalar setting consisting of a two-layered background medium imaged via a multifrequency, multimonostatic configuration. The objective is to reduce data for a subsurface imaging problem without performance degradation by determining the optimal sensor locations in both spatial and frequency domains. In this regard, we present a sampling method that effectively extends the maximal projection on minimum eigenspace (MPME) algorithm to tackle the semidiscrete inverse problem associated with subsurface imaging. Compared to the state-of-the-art technique, we significantly reduce the required samples for imaging. Numerical and experiment results, the latter concerning a buried water pipe, are reported to demonstrate the effectiveness of the proposed sampling strategy. In particular, for the considered cases, the proposed sampling method shows a data reduction of more than 50% compared to other literature sampling methods.