Translator Disclaimer
29 April 2010 Locally adaptive detection algorithm for forward-looking ground-penetrating radar
Author Affiliations +
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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Timothy C. Havens, K. C. Ho, Justin Farrell, James M. Keller, Mihail Popescu, Tuan T. Ton, David C. Wong, and Mehrdad Soumekh "Locally adaptive detection algorithm for forward-looking ground-penetrating radar", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76642E (29 April 2010);

Back to Top