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Proceedings Paper

Improved electromagnetic induction processing with novel adaptive matched filter and matched subspace detection
Author(s): Charles Ethan Hayes; James H. McClellan; Waymond R. Scott Jr.; Andrew J. Kerr
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Paper Abstract

This work introduces two advances in wide-band electromagnetic induction (EMI) processing: a novel adaptive matched filter (AMF) and matched subspace detection methods. Both advances make use of recent work with a subspace SVD approach to separating the signal, soil, and noise subspaces of the frequency measurements The proposed AMF provides a direct approach to removing the EMI self-response while improving the signal to noise ratio of the data. Unlike previous EMI adaptive downtrack filters, this new filter will not erroneously optimize the EMI soil response instead of the EMI target response because these two responses are projected into separate frequency subspaces. The EMI detection methods in this work elaborate on how the signal and noise subspaces in the frequency measurements are ideal for creating the matched subspace detection (MSD) and constant false alarm rate matched subspace detection (CFAR) metrics developed by Scharf The CFAR detection metric has been shown to be the uniformly most powerful invariant detector.

Paper Details

Date Published: 3 May 2016
PDF: 14 pages
Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98230E (3 May 2016); doi: 10.1117/12.2224681
Show Author Affiliations
Charles Ethan Hayes, Georgia Institute of Technology (United States)
James H. McClellan, Georgia Institute of Technology (United States)
Waymond R. Scott Jr., Georgia Institute of Technology (United States)
Andrew J. Kerr, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9823:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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