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Proceedings Paper

Locally adaptive detection algorithm for forward-looking ground-penetrating radar
Author(s): Timothy C. Havens; K. C. Ho; Justin Farrell; James M. Keller; Mihail Popescu; Tuan T. Ton; David C. Wong; Mehrdad Soumekh
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Paper Abstract

This paper proposes an effective anomaly detection algorithm for a forward-looking ground-penetrating radar (FLGPR). One challenge for threat detection using FLGPR is its high dynamic range in response to different kinds of targets and clutter objects. The application of a fixed threshold for detection often yields a large number of false alarms. We propose a locally-adaptive detection method that adjusts the detection criteria automatically and dynamically across different spatial regions, which improves the detection of weak scattering targets. The paper also examines a spectrum-based classifier. This classifier rejects false alarms (FAs) by classifying each alarm location based on its spatial frequency-spectrum. Experimental results for the improved detection techniques are demonstrated by field data measurements from a US Army test site.

Paper Details

Date Published: 29 April 2010
PDF: 9 pages
Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76642E (29 April 2010); doi: 10.1117/12.851512
Show Author Affiliations
Timothy C. Havens, Univ. of Missouri-Columbia (United States)
K. C. Ho, Univ. of Missouri-Columbia (United States)
Justin Farrell, Univ. of Missouri-Columbia (United States)
James M. Keller, Univ. of Missouri-Columbia (United States)
Mihail Popescu, Univ. of Missouri-Columbia (United States)
Tuan T. Ton, U.S. Army Night Vision and Electronic Sensors Directorate (United States)
David C. Wong, U.S. Army Night Vision and Electronic Sensors Directorate (United States)
Mehrdad Soumekh, Univ. at Buffalo (United States)


Published in SPIE Proceedings Vol. 7664:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV
Russell S. Harmon; John H. Holloway; J. Thomas Broach, Editor(s)

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