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10 May 2019 Classification using low-rank features from an electromagnetic induction sensor
Waymond R. Scott Jr., Charles Ethan Hayes, James H. McClellan
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Proceedings Volume 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV; 110120R (2019) https://doi.org/10.1117/12.2519362
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States
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.
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Waymond R. Scott Jr., Charles Ethan Hayes, and James H. McClellan "Classification using low-rank features from an electromagnetic induction sensor", Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 110120R (10 May 2019); https://doi.org/10.1117/12.2519362
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KEYWORDS
Sensors
Data modeling
Magnetism
Soil science
Electromagnetism
Target detection
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