Unexploded ordnance (UXO) presents a challenge to active military installations seeking to manage and clean their test and training ranges, as well as to formerly used defense sites and sites designated for base realignment and closure that are attempting UXO remediation. In the United States alone, more than 900 sites with varying terrain, foliation, and topography are potentially contaminated with UXO. Using current technologies, the cost of identifying and disposing of UXO in the U.S. is estimated to range up to $500 billion. New technologies capable of identifying UXO with high detection rates and low incidence of false alarm rates are required to drastically reduce the cost of site cleanup.
The objective of this project is to develop a reliable technique for discriminating between buried UXO and clutter using multisensor electromagnetic induction (EMI) sensor array data. The effort builds on existing research which exploits the differences in shape between ordnance and clutter and the effects of other distinctive properties of ordnance items such as fuze bodies, driving bands, and fin assemblies.
Specifically, the project intends to perform tests with EMI sensors that operate at less than 100 KHz. The effort will develop (1) models for the ordnance signature and its constituent parts; (2) procedures for determining target characteristics from multisensor data using the signature models; and (3) decision rules for discriminating between buried UXO and clutter.
Broadly, progress has been made in four areas: (1) calibrating and evaluating the GEM3 instrument, (2) evaluating the informational content of data from the GEM3, (3) developing a model for EMI response from a variety of shapes, and (4) evaluating inversion techinques to extract model parameters from EMI data.
The goal of this research is to produce processing algorithms and procedures in order to utilize existing sensor technologies within the less than 100 KHz domain. A benefit of this technology is its ability to optimize EMI sensor array configuration and effectively process algorithms for EMI data. This technology may be directly transitioned to modify the MTADS platform and data analysis system. In addition, this research may prove to be useful for commercial survey work by introducing new measurement techniques and processing algorithms.