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Proceedings Paper

A new EMI system for detection and classification of challenging targets
Author(s): F. Shubitidze; J. P. Fernández; B. E. Barrowes; K. O'Neill
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Paper Abstract

Advanced electromagnetic induction (EMI) sensors currently feature multi-axis illumination of targets and tri-axial vector sensing (e.g., MetalMapper), or exploit multi-static array data acquisition (e.g., TEMTADS). They produce data of high density, quality, and diversity, and have been combined with advanced EMI models to provide superb classification performance relative to the previous generation of single-axis, monostatic sensors. However, these advances yet have to improve significantly our ability to classify small, deep, and otherwise challenging targets. Particularly, recent live-site discrimination studies at Camp Butner, NC and Camp Beale, CA have revealed that it is more challenging to detect and discriminate small munitions (with calibers ranging from 20 mm to 60 mm) than larger ones. In addition, a live-site test at the Massachusetts Military Reservation, MA highlighted the difficulties for current sensors to classify large, deep, and overlapping targets with high confidence. There are two main approaches to overcome these problems: 1) adapt advanced EMI models to the existing systems and 2) improve the detection limits of current sensors by modifying their hardware. In this paper we demonstrate a combined software/hardware approach that will provide extended detection range and spatial resolution to next-generation EMI systems; we analyze and invert EMI data to extract classification features for small and deep targets; and we propose a new system that features a large transmitter coil.

Paper Details

Date Published: 7 June 2013
PDF: 8 pages
Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 870906 (7 June 2013); doi: 10.1117/12.2016349
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)
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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