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Proceedings Paper

Fourier features for explosive hazard detection using a wideband electromagnetic induction sensor
Author(s): Brendan Alvey; Dominic K. C. Ho; Alina Zare
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Paper Abstract

Sensors which use electromagnetic induction (EMI) to excite a response in conducting bodies have been investigated for the purpose of detecting buried explosives. In particular, wide band EMI sensors which use a relatively low number of operating frequencies have been used to discriminate between types of objects, and to detect objects with very low metal content.1 In this paper, Fourier features are extracted using the 2D Fourier transform from the complex data; both spatially, and across operating frequencies. Then, the Multiple Instance Adaptive Coherence Estimator (MI-ACE)2 is used to learn cross-validated target signatures from these features. These signatures, as well as learned background, statistics are used with ACE to generate confidence maps, which are clustered into alarms. Alarms are scored against a ground truth and compared to other detection algorithms.

Paper Details

Date Published: 3 May 2017
PDF: 9 pages
Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 101820E (3 May 2017); doi: 10.1117/12.2263019
Show Author Affiliations
Brendan Alvey, Univ. of Missouri (United States)
Dominic K. C. Ho, Univ. of Missouri (United States)
Alina Zare, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 10182:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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