Paper
3 May 2016 Improved electromagnetic induction processing with novel adaptive matched filter and matched subspace detection
Charles Ethan Hayes, James H. McClellan, Waymond R. Scott Jr., Andrew J. Kerr
Author Affiliations +
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.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Charles Ethan Hayes, James H. McClellan, Waymond R. Scott Jr., and Andrew J. Kerr "Improved electromagnetic induction processing with novel adaptive matched filter and matched subspace detection", Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98230E (3 May 2016); https://doi.org/10.1117/12.2224681
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Electromagnetic coupling

Interference (communication)

Sensors

Signal to noise ratio

Signal detection

Target detection

Data modeling

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