Statistical Signal Processing with Physics-Based Models: UXO Detection and Identification

摘要

Using current technologies, the cost of identifying and disposing of UXO in the United States is estimated to range up to $500 billion. Site specific costs range from $400/acre for surface UXO to $1.4 million/acre for subsurface UXO. There are 1900 Formerly Used Defense Sites (FUDS) and 130 Base Realignment and Closure (BRAC) installations that need to be cleared. Several sensor modalities are currently being explored for the detection and identification of surface and buried UXO. These include electromagnetic induction (EMI), magnetometers, radar, and seismic sensors. These sensors experience little difficulty detecting the UXO, thus detection does not create the bottleneck that results in the high cost of remediating sites. The primary contributor to the costs and time associated with remediating a UXO contaminated site is the high false-alarm rate associated with each of the sensors when operated individually. In this project, we investigated the phenomenological aspects of the UXO detection, location, and discrimination problem using EMI, radar, and magnetometer sensors. The fundamental insight garnered by characterizing the underlying physics was transitioned into performance bounds, as well as high-performance sensor fusion and signal processing algorithms for enhanced detection, location, and discrimination of buried UXO under a wide range of environmental conditions. The signal processing algorithms that were developed were evaluated on data collected in the field during two government-sponsored sensor demonstrations. Finally, the algorithms were applied to a blind data set and performance was assessed by an independent agency.