18 May 2006 Constrained filter optimization for subsurface landmine detection
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Abstract
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 are discussed.
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Peter A. Torrione, Leslie Collins, Fred Clodfelter, Dan Lulich, Ajay Patrikar, Peter Howard, Richard Weaver, Erik Rosen, "Constrained filter optimization for subsurface landmine detection", Proc. SPIE 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI, 62172X (18 May 2006); doi: 10.1117/12.665780; https://doi.org/10.1117/12.665780
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