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Proceedings Paper

Magnetic sensing techniques for humanitarian ordnance detection and discrimination
Author(s): Joe Keranen; Steve Billings; Gregory Schultz; Jonathan Miller
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Paper Abstract

Detection and discrimination of unexploded ordnance (UXO) in areas of prior conflict is of high importance to the international community and the United States government. For humanitarian applications, sensors and processing methods need to be robust, reliable, and easy to train and implement using indigenous UXO removal personnel. This paper focuses on magnetometer sensing techniques, processing, and operation for UXO detection and discrimination applications. Specifically, we discuss the collection, processing, and discrimination of data collected using the PACMAG man-portable system consisting of arrays of sensitive total-field magnetometers, global positioning (GPS) combined with digital odometers, and a data acquisition system. We outline preliminary standard operating procedures for optimal collection of UXO-induced magnetic fields and associated position data using either a GPS, or odometer when surveying in GPS-denied areas. Processing techniques such as gridding and filtering, target picking, and discrimination lead to estimates of target size and location. Emphasis is placed on simplifying the production of magnetometer hardware and software for use by minimally-trained personnel with no advanced knowledge of magnetic sensing and geophysics.

Paper Details

Date Published: 23 May 2011
PDF: 9 pages
Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 801703 (23 May 2011);
Show Author Affiliations
Joe Keranen, Sky Research, Inc. (United States)
Steve Billings, Sky Research, Inc. (United States)
Gregory Schultz, Sky Research, Inc. (United States)
Jonathan Miller, Sky Research, Inc. (United States)


Published in SPIE Proceedings Vol. 8017:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI
Russell S. Harmon; John H. Holloway Jr.; J. Thomas Broach, Editor(s)

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