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Classification using low-rank features from an electromagnetic induction sensor
Author(s): Waymond R. Scott Jr.; Charles Ethan Hayes; James H. McClellan
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Paper Abstract

A method for classifying targets using a low-rank representation of broadband electromagnetic induction data is presented. The method does not require position data, a sensor model, or a complex inversion so it is applicable to hand-held EMI systems or a simple vehicle-based system. The low-rank representation is very straightforward to compute and does not require position significant computational resources. The method will be shown for data from a cart-based Georgia Tech EMI sensor that operates in the frequency domain and collects data at 15 logarithmically spaced frequencies from 1 kHz to 90 kHz. The data for several will be presented in the low-rank form to show that they are consistent within a target type and distinct for different targets. An example using the low-rank data to classify targets will be presented.

Paper Details

Date Published: 10 May 2019
PDF: 16 pages
Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 110120R (10 May 2019); doi: 10.1117/12.2519362
Show Author Affiliations
Waymond R. Scott Jr., Georgia Institute of Technology (United States)
Charles Ethan Hayes, Georgia Institute of Technology (United States)
James H. McClellan, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 11012:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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