Sensor fusion for buried explosive threat detection for handheld data

Published

Conference Paper

© 2017 SPIE. 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.

Full Text

Duke Authors

Cited Authors

  • Knox, M; Rundel, C; Collins, L

Published Date

  • January 1, 2017

Published In

Volume / Issue

  • 10182 /

Electronic International Standard Serial Number (EISSN)

  • 1996-756X

International Standard Serial Number (ISSN)

  • 0277-786X

International Standard Book Number 13 (ISBN-13)

  • 9781510608658

Digital Object Identifier (DOI)

  • 10.1117/12.2263013

Citation Source

  • Scopus