Ground Penetrating Synthetic Aperture Imaging of Subsurface Targets

Abstract. Motivated by the use of unmanned aerial vehicles (UAVs) equipped with ground penetrating synthetic aperture radar (GP-SAR) for imaging buried landmines, we study imaging of subsurface targets. A key challenge in this problem is the removal of so-called ground bounce signals which are reflections from the air-soil interface. When ground bounce signals can be removed exactly from GP-SAR measurements, one can readily image subsurface targets using traditional synthetic aperture imaging methods such as Kirchhoff migration (KM). However, ground bounce signals dominate measurements, carry no useful information about subsurface targets, and disrupt their imaging. In this paper we present an asymptotic theory for this problem assuming that the elevation of the UAV flight path is the largest length scale in the problem. Using this asymptotic theory, we introduce data-driven methods for (i) estimating the ground permittivity, and (ii) approximately removing ground bounce signals from GP-SAR. We first establish these methods for the simple case of a flat air-soil interface and then apply them to an unknown rough air-soil interface. We validate this asymptotic theory using numerical simulations.