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