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Proceedings Paper

A comparison of robust principal component analysis techniques for buried object detection in downward looking GPR sensor data
Author(s): Anthony Pinar; Timothy C. Havens; Joseph Rice; Matthew Masarik; Joseph Burns; Brian Thelen
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Paper Abstract

Explosive hazards are a deadly threat in modern conflicts; hence, detecting them before they cause injury or death is of paramount importance. One method of buried explosive hazard discovery relies on data collected from ground penetrating radar (GPR) sensors. Threat detection with downward looking GPR is challenging due to large returns from non-target objects and clutter. This leads to a large number of false alarms (FAs), and since the responses of clutter and targets can form very similar signatures, classifier design is not trivial. One approach to combat these issues uses robust principal component analysis (RPCA) to enhance target signatures while suppressing clutter and background responses, though there are many versions of RPCA. This work applies some of these RPCA techniques to GPR sensor data and evaluates their merit using the peak signal-to-clutter ratio (SCR) of the RPCA-processed B-scans. Experimental results on government furnished data show that while some of the RPCA methods yield similar results, there are indeed some methods that outperform others. Furthermore, we show that the computation time required by the different RPCA methods varies widely, and the selection of tuning parameters in the RPCA algorithms has a major effect on the peak SCR.

Paper Details

Date Published: 3 May 2016
PDF: 16 pages
Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98230T (3 May 2016); doi: 10.1117/12.2229076
Show Author Affiliations
Anthony Pinar, Michigan Technological Univ. (United States)
Timothy C. Havens, Michigan Technological Univ. (United States)
Joseph Rice, Michigan Technological Univ. (United States)
Matthew Masarik, Michigan Technological Univ. (United States)
Joseph Burns, Michigan Technological Univ. (United States)
Brian Thelen, Michigan Technological Univ. (United States)

Published in SPIE Proceedings Vol. 9823:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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