Multiview imaging for low-signature target detection in rough-surface clutter environment

Forward-looking ground-penetrating radar (FL-GPR) permits standoff sensing of shallow in-road threats. A major challenge facing this radar technology is the high rate of false alarms stemming from the vulnerability of the target responses to interference scattering arising from interface roughness and subsurface clutter. In this paper, we present a multiview approach for target detection in FL-GPR. Various images corresponding to the different views are generated using a tomographic algorithm, which considers the near-field nature of the sensing problem. Furthermore, for reducing clutter and maintaining high cross-range resolution over the imaged area, each image is computed in a segmentwise fashion using coherent integration over a suitable set of measurements from multiple platform positions. We employ two fusion approaches based on likelihood ratio tests detector to combine the multiview images for enhanced target detection. The superior performance of the multiview approach over single-view imaging is demonstrated using electromagnetic modeling data.