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Proceedings Paper

Deep belief networks for false alarm rejection in forward-looking ground-penetrating radar
Author(s): John Becker; Timothy C. Havens; Anthony Pinar; Timothy J. Schulz
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Paper Abstract

Explosive hazards are one of the most deadly threats in modern conflicts. The U.S. Army is interested in a reliable way to detect these hazards at range. A promising way of accomplishing this task is using a forward-looking ground-penetrating radar (FLGPR) system. Recently, the Army has been testing a system that utilizes both L-band and X-band radar arrays on a vehicle mounted platform. Using data from this system, we sought to improve the performance of a constant false-alarm-rate (CFAR) prescreener through the use of a deep belief network (DBN). DBNs have also been shown to perform exceptionally well at generalized anomaly detection. They combine unsupervised pre-training with supervised fine-tuning to generate low-dimensional representations of high-dimensional input data. We seek to take advantage of these two properties by training a DBN on the features of the CFAR prescreener’s false alarms (FAs) and then use that DBN to separate FAs from true positives. Our analysis shows that this method improves the detection statistics significantly. By training the DBN on a combination of image features, we were able to significantly increase the probability of detection while maintaining a nominal number of false alarms per square meter. Our research shows that DBNs are a good candidate for improving detection rates in FLGPR systems.

Paper Details

Date Published: 15 May 2015
PDF: 12 pages
Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 94540W (15 May 2015); doi: 10.1117/12.2176855
Show Author Affiliations
John Becker, Michigan Technological Univ. (United States)
Timothy C. Havens, Michigan Technological Univ (United States)
Anthony Pinar, Michigan Technological Univ. (United States)
Timothy J. Schulz, Michigan Technological Univ. (United States)


Published in SPIE Proceedings Vol. 9454:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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