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Proceedings Paper

Target localization and signature extraction in GPR data using expectation-maximization and principal component analysis
Author(s): Daniel Reichman; Kenneth D. Morton; Leslie M. Collins; Peter A. Torrione
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Paper Abstract

Ground Penetrating Radar (GPR) is a very promising technology for subsurface threat detection. A successful algorithm employing GPR should achieve high detection rates at a low false-alarm rate and do so at operationally relevant speeds. GPRs measure reflections at dielectric boundaries that occur at the interfaces between different materials. These boundaries may occur at any depth, within the sensor's range, and furthermore, the dielectric changes could be such that they induce a 180 degree phase shift in the received signal relative to the emitted GPR pulse. As a result of these time-of-arrival and phase variations, extracting robust features from target responses in GPR is not straightforward. In this work, a method to mitigate polarity and alignment variations based on an expectation-maximization (EM) principal-component analysis (PCA) approach is proposed. This work demonstrates how model-based target alignment can significantly improve detection performance. Performance is measured according to the improvement in the receiver operating characteristic (ROC) curve for classification before and after the data is properly aligned and phase-corrected.

Paper Details

Date Published: 29 May 2014
PDF: 12 pages
Proc. SPIE 9072, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX, 90720Q (29 May 2014); doi: 10.1117/12.2049874
Show Author Affiliations
Daniel Reichman, Duke Univ. (United States)
Kenneth D. Morton, Duke Univ. (United States)
Leslie M. Collins, Duke Univ. (United States)
Peter A. Torrione, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 9072:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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