Sensor Arrangement in Monostatic Subsurface Radar Imaging

This paper deals with microwave subsurface imaging achieved by inverting the linearized scattering operator. The focus is on the determination of a strategy for spatially sampling the data which allows to reduce the spatial data measurements and at the same time to keep the same achievable performance in the reconstructions. To this end, the measurement points are determined in order to approximate the point-spread function corresponding to the ideal continuous case (i.e., the case in which the data space is not sampled at all). For the sake of simplicity, the study is developed for a 2D scalar configuration. Also, the standard mono-static measurement arrangement is considered. However, in order to mimic a subsurface imaging scenario, a two-layered background medium is addressed. The main idea is to introduce suitable variable transformations which allow to express the point-spread functions as a Fourier-like transformation; this then provides insights for devising the sampling scheme. It is shown the resulting measurement spatial positions must be non-uniformly arranged across the measurement domain and their number can be much lower than the one provided by some commonly used literature sampling criteria