Three-dimensional features, based on beamforming at multiple depths, improves landmine detection with a forward-looking ground-penetrating radar

Abstract

Forward-looking ground-penetrating radar (FLGPR) has been investigated as a remote sensing modality for buried threat detection (e.g., landmines). Generally in this context, raw FLGPR data is beamformed into two-dimensional images and then automated algorithms are applied to detect buried threats. The beamformed images represent a crude estimate of the energy reflected from the subsurface (i) across a particular spatial extent, and (ii) at a particular depth. Typically, a single focus depth (e.g., −0.25 meters) is used to beamform all of the imagery. In this work we hypothesize that this approach disregards important factors such as the likely variable burial depth of threats, and varying contours of the ground. We examine this hypothesis by beamforming multiple images, each at a separate focus depth, and stack them to form 3-dimensional (3D) volumes. Several popular FLGPR detection algorithms are applied to both the 3D volumes and the conventional 2D images on a large collection of FLGPR data. The results show that the 3D detectors consistently outperform their 2D counterparts. This suggests that the use of a single beamforming focus depth for imaging is a potential performance bottleneck for threat detection in FLGPR data.