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Proceedings Paper

Improving detection of low-metallic-content land mines using EMI data
Author(s): Leslie M. Collins; Stacy L. Tantum; Ping Gao; John David Moulton; Larry Makowsky; Denis Reidy; Richard C. Weaver
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Paper Abstract

EMI sensors are used extensively to detect landmines, and operate by detecting the metal that is present in mines. However, mines vary in their construction form metal-cased varieties with a large mass of metal to plastic-cased varieties with minute amounts of metal. Unfortunately, there is often a significant amount of metallic clutter present in the environment. Consequently, EMI sensors that utilize traditional detection algorithms based solely on metal content suffer form large false alarm rates. We have at least partially mitigated this false alarm problem for high- metal content mines by developing statistical algorithms that exploit phenomenological models of the underlying physics. The Joint UXO Coordination Office (JUXOCU) is sponsoring a series of experiments designed to establish a performance baseline for a variety of sensors. The experiments to dat have focused on detection and discrimination of low-metallic content mines. This baseline will be used to measure the potential improvements in performance offered by advanced signal processing algorithms This paper describes the result of several experiments performed in conjunction with the JUXOCU effort. In our preliminary work, statistical algorithms have been applied specifically to the problem of detection of low-metal mines, and dramatic performance improvements have been observed with respect to the baseline performance. However, these algorithms improvements have been observed with respect to the baseline performance. However, these algorithms were statistical in nature, did not incorporate phenomenological models, and exploited spatial information. The tradeoffs among these various factors are explored in this paper, along with the performance of alternative statistical approaches. In addition, approaches to classification of the mine-type are discussed and the performance of such classifiers is presented.

Paper Details

Date Published: 22 August 2000
PDF: 11 pages
Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); doi: 10.1117/12.396241
Show Author Affiliations
Leslie M. Collins, Duke Univ. (United States)
Stacy L. Tantum, Duke Univ. (United States)
Ping Gao, Duke Univ. (United States)
John David Moulton, U.S. Army Joint UXO Coordination Office (United States)
Larry Makowsky, U.S. Army Joint UXO Coordination Office (United States)
Denis Reidy, U.S. Army Joint UXO Coordination Office (United States)
Richard C. Weaver, U.S. Army Joint UXO Coordination Office (United States)


Published in SPIE Proceedings Vol. 4038:
Detection and Remediation Technologies for Mines and Minelike Targets V
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

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