Processing Techniques for Discrimination Between Buried UXO and Clutter Using Multisensor Array Data

Abstract : This project has addressed the issue of discriminating between buried unexploded ordnance (UXO) and clutter in the context of environmental cleanup. With traditional survey methodology ( Mag and Flag methods with hand-held detectors operated by explosives ordnance disposal (EOD) or civilian UXO technicians), the Army Corps of Engineers finds that 85-95% of all detected objects are not UXO. In the last decade, modern UXO detection surveys conducted with digital systems and georeferenced positioning have consistently demonstrated superior detection capabilities over Mag and Flag . However, in spite of the recent advances in UXO detection performance, false alarms due to clutter still remain a serious problem. Because the cost of identifying and disposing of UXO in the United States using current technologies is estimated to range up to $500 billion, increases in performance efficiency due to reduced false alarm rates can result in substantial cost savings. Unlike clutter, which can have any shape and composition, UXO are typically long and slender and are composed of a steel body with a brass or aluminum fuze body and copper driving bands. These physical attributes produce distinctive signatures in electromagnetic induction (EMI) sensor data. This project was aimed at systematically exploring the performance improvements that may be realized using EMI sensors when distinguishing target attributes are included in the discrimination process, amid contribution from competing signal sources under field conditions. The overall objective of this project was to develop reliable techniques for discriminating between buried UXO and clutter using multisensor EMI array data. The goal was to build on existing research that exploits differences in shape between ordnance and clutter by including the effects of other distinctive properties of ordnance items (fuze bodies, driving bands, fin assemblies, etc.).