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Proceedings Paper

Sensor fusion for buried explosive threat detection for handheld data
Author(s): Mary Knox; Colin Rundel; Leslie Collins
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Paper Abstract

Data from multiple sensors has been collected using a handheld system, and includes precise location information. These sensors include ground penetrating radar (GPR) and electromagnetic induction (EMI) sensors. The performance of these sensors on different mine-types varies considerably. For example, the EMI sensor is effective at locating relatively small mines with metal while the GPR sensor is able to easily detect large plastic mines. In this work, we train linear (logistic regression) and non-linear (gradient boosting decision trees) methods on the EMI and GPR data in order to improve buried explosive threat detection performance.

Paper Details

Date Published: 3 May 2017
PDF: 8 pages
Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 101820D (3 May 2017); doi: 10.1117/12.2263013
Show Author Affiliations
Mary Knox, Duke Univ. (United States)
Colin Rundel, Duke Univ. (United States)
Leslie Collins, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 10182:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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