Development of a statistical model (tool) for classifying buried object detectability

Abstract

To improve warfighter safety under changing requirements, assets must be adaptable and able to detect and mark buried hazards. Current GPR systems would benefit by improvements in speed, usability, and operational versatility to enhance operational effectiveness for all platforms and missions. Our sponsor requests that LLNL research GPR methodologies and approaches to allow for specific improvements to be made in those areas. MiRadar uses a novel multi-static array architecture coupled with tomographic processing to focus returned energy from the real (physical) and synthetic (motion-derived) apertures. 3D streaming movies of the subsurface are easily interpreted, slashing training times. Real-time processing codes enable imaging and operation three times faster; automated volumetric detection algorithms warn of possible hazards. In a two-week trial, multiple vehicle-mounted arrays and a new ultralightweight architecture on a small, unmanned aircraft system executed 498 runs, collecting over 400km of data on 13 preemplaced lanes containing 384 simulated buried threats.