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Proceedings Paper

Target-classification approach applied to active UXO sites
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Paper Abstract

This study is designed to illustrate the discrimination performance at two UXO active sites (Oklahoma’s Fort Sill and the Massachusetts Military Reservation) of a set of advanced electromagnetic induction (EMI) inversion/discrimination models which include the orthonormalized volume magnetic source (ONVMS), joint diagonalization (JD), and differential evolution (DE) approaches and whose power and flexibility greatly exceed those of the simple dipole model. The Fort Sill site is highly contaminated by a mix of the following types of munitions: 37-mm target practice tracers, 60-mm illumination mortars, 75-mm and 4.5′′ projectiles, 3.5′′, 2.36′′, and LAAW rockets, antitank mine fuzes with and without hex nuts, practice MK2 and M67 grenades, 2.5′′ ballistic windshields, M2A1-mines with/without bases, M19-14 time fuzes, and 40-mm practice grenades with/without cartridges. The site at the MMR site contains targets of yet different sizes. In this work we apply our models to EMI data collected using the MetalMapper (MM) and 2 × 2 TEMTADS sensors. The data for each anomaly are inverted to extract estimates of the extrinsic and intrinsic parameters associated with each buried target. (The latter include the total volume magnetic source or NVMS, which relates to size, shape, and material properties; the former includes location, depth, and orientation). The estimated intrinsic parameters are then used for classification performed via library matching and the use of statistical classification algorithms; this process yielded prioritized dig-lists that were submitted to the Institute for Defense Analyses (IDA) for independent scoring. The models’ classification performance is illustrated and assessed based on these independent evaluations.

Paper Details

Date Published: 7 June 2013
PDF: 8 pages
Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 870907 (7 June 2013); doi: 10.1117/12.2016351
Show Author Affiliations
F. Shubitidze, Thayer School of Engineering at Dartmouth (United States)
White River Technologies, Inc. (United States)
J. P. Fernández, Thayer School of Engineering at Dartmouth (United States)
Irma Shamatava, Thayer School of Engineering at Dartmouth (United States)
White River Technologies, Inc. (United States)
B. E. Barrowes, Thayer School of Engineering at Dartmouth (United States)
U.S. Army Engineer Research and Development Ctr. (United States)
K. O'Neill, Thayer School of Engineering at Dartmouth (United States)


Published in SPIE Proceedings Vol. 8709:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII
J. Thomas Broach; Jason C. Isaacs, Editor(s)

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