Locally adaptive detection algorithm for forward-looking ground-penetrating radar

Timothy C. Havens; K. C. Ho; Justin Farrell; James M. Keller; Mihail Popescu; Tuan T. Ton; David C. Wong; Mehrdad Soumekh

doi:10.1117/12.851512

29 April 2010 Locally adaptive detection algorithm for forward-looking ground-penetrating radar

Timothy C. Havens, K. C. Ho, Justin Farrell, James M. Keller, Mihail Popescu, Tuan T. Ton, David C. Wong, Mehrdad Soumekh

Author Affiliations +

Proceedings Volume 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV; 76642E (2010) https://doi.org/10.1117/12.851512
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

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.

Citation Download Citation

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); https://doi.org/10.1117/12.851512

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