A large-scale multi-institutional evaluation of advanced discrimination algorithms for buried threat detection in ground penetrating radar

Published

Journal Article

© 1980-2012 IEEE. In this paper, we consider the development of algorithms for the automatic detection of buried threats using ground penetrating radar (GPR) measurements. GPR is one of the most studied and successful modalities for automatic buried threat detection (BTD), and a large variety of BTD algorithms have been proposed for it. Despite this, large-scale comparisons of GPR-based BTD algorithms are rare in the literature. In this paper, we report the results of a multi-institutional effort to develop advanced BTD algorithms for a real-world GPR BTD system. The effort involved five institutions with substantial experience with the development of GPR-based BTD algorithms. In this paper, we report the technical details of the advanced algorithms submitted by each institution, representing their latest technical advances, and many state-of-the-art GPR-based BTD algorithms. We also report the results of evaluating the algorithms from each institution on the large experimental data set used for development. The experimental data set comprised 120 000 m2 of GPR data using surface area, from 13 different lanes across two U.S. test sites. The data were collected using a vehicle-mounted GPR system, the variants of which have supplied data for numerous publications. Using these results, we identify the most successful and common processing strategies among the submitted algorithms, and make recommendations for GPR-based BTD algorithm design.

Full Text

Duke Authors

Cited Authors

  • Malof, JM; Reichman, D; Karem, A; Frigui, H; Ho, KC; Wilson, JN; Lee, WH; Cummings, WJ; Collins, LM

Published Date

  • September 1, 2019

Published In

Volume / Issue

  • 57 / 9

Start / End Page

  • 6929 - 6945

Electronic International Standard Serial Number (EISSN)

  • 1558-0644

International Standard Serial Number (ISSN)

  • 0196-2892

Digital Object Identifier (DOI)

  • 10.1109/TGRS.2019.2909665

Citation Source

  • Scopus