Constrained filter optimization for subsurface landmine detection


Conference Paper

Previous large-scale blind tests of anti-tank landmine detection utilizing the NIITEK ground penetrating radar indicated the potential for very high anti-tank landmine detection probabilities at very low false alarm rates for algorithms based on adaptive background cancellation schemes. Recent data collections under more heterogeneous multi-layered road-scenarios seem to indicate that although adaptive solutions to background cancellation are effective, the adaptive solutions to background cancellation under different road conditions can differ significantly, and misapplication of these adaptive solutions can reduce landmine detection performance in terms of PD/FAR. In this work we present a framework for the constrained optimization of background-estimation filters that specifically seeks to optimize PD/FAR performance as measured by the area under the ROC curve between two FARs. We also consider the application of genetic algorithms to the problem of filter optimization for landmine detection. Results indicate robust results for both static and adaptive background cancellation schemes, and possible real-world advantages and disadvantages of static and adaptive approaches arc discussed.

Full Text

Duke Authors

Cited Authors

  • Torrione, PA; Collins, L; Clodfelter, F; Lulich, D; Patrikar, A; Howard, P; Weaver, R; Rosen, E

Published Date

  • August 23, 2006

Published In

Volume / Issue

  • 6217 II /

International Standard Serial Number (ISSN)

  • 0277-786X

International Standard Book Number 10 (ISBN-10)

  • 081946273X

International Standard Book Number 13 (ISBN-13)

  • 9780819462732

Digital Object Identifier (DOI)

  • 10.1117/12.665780

Citation Source

  • Scopus