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Proceedings Paper

Dealing with clutter in EMI inversion and classification schemes for buried UXO discrimination
Author(s): Kevin O'Neill; Keli Sun; Fridon Shubitidze; Irma Shamatava; Lanbo Liu; Keith D. Paulsen
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Paper Abstract

Virtually all signal processing strategies for discrimination of buried UXO are clutter limited. Most buried UXO are to be found in the top meter of soil, and therefore produce detectable electromagnetic responses. However they also typically reside in settings with widespread metallic clutter from detonated ordnance or other sources. While generally smaller than the UXO, metallic fragments can be numerous and may be shallower than the UXO we seek. Thus clutter signals may be stronger than those from UXO, especially locally, and may cause either highly localized or diffuse obscuration of signatures. They may mask crucial UXO frequency and temporal response patterns, and may distort the otherwise revealing spatial variations of response. To deal with this, first an analytical physical model of electromagnetic induction (EMI) scattering from widespread metallic clutter is formulated and tested. The dependence of signal magnitude on antenna elevation is determined for both thin surface layers and volume layers of clutter. This dependence is different from that of a single UXO size target. In treatment of UWB EMI measurements, this difference is exploited to elicit evidence of the UXO-like target when it is screened from the sensor by a surface layer of small metallic objects. Inversions are also performed for characterizing the geometry of a UXO-like target beneath a surface layer of clutter. The test cases compare a simple least squares (SLS) and a Bayesian-inspired statistical (BIS) approach. As target depth is increased and signal to clutter ratio decreases, the BIS generally produces more consistent and accurate results.

Paper Details

Date Published: 11 September 2003
PDF: 12 pages
Proc. SPIE 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII, (11 September 2003); doi: 10.1117/12.487287
Show Author Affiliations
Kevin O'Neill, U.S. Army Engineer Research & Development Ctr. (United States)
Keli Sun, Dartmouth College (United States)
Fridon Shubitidze, Dartmouth College (United States)
Irma Shamatava, Dartmouth College (United States)
Lanbo Liu, U.S. Army Engineer Research & Development Ctr. (United States)
Keith D. Paulsen, Dartmouth College (United States)


Published in SPIE Proceedings Vol. 5089:
Detection and Remediation Technologies for Mines and Minelike Targets VIII
Russell S. Harmon; John H. Holloway; J. T. Broach, Editor(s)

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