Automated calibration methods for robotic multisensor landmine detection

Abstract

Both force protection and humanitarian demining missions require efficient and reliable detection and discrimination of buried anti-tank and anti-personnel landmines. Widely varying surface and subsurface conditions, mine types and placement, as well as environmental regimes challenge the robustness of the automatic target recognition process. In this paper we present applications created for the U.S. Army Nemesis detection platform. Nemesis is an unmanned rubber-tracked vehicle-based system designed to eradicate a wide variety of anti-tank and anti-personnel landmines for humanitarian demining missions. The detection system integrates advanced ground penetrating synthetic aperture radar (GPSAR) and electromagnetic induction (EMI) arrays, highly accurate global and local positioning, and on-board target detection/classification software on the front loader of a semi-autonomous UGV. An automated procedure is developed to estimate the soil's dielectric constant using surface reflections from the ground penetrating radar. The results have implications not only for calibration of system data acquisition parameters, but also for user awareness and tuning of automatic target recognition detection and discrimination algorithms.