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Proceedings Paper

Hyperbolic and PLSDA filter algorithms to detect buried threats in GPR data
Author(s): Dmitry Kalika; Kenneth D. Morton Jr.; Leslie M. Collins; Peter A. Torrione
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Paper Abstract

Ground Penetrating radar (GPR) is a commonly used modality for the detection of buried threats. This work explores two approaches for buried threat detection in GPR data that we refer to as the hyperbolic filter and PLSDA filter algorithms. The hyperbolic filter algorithm leverages the hyperbolic shape of buried threat GPR responses, while the PLSDA algorithm uses a PLSDA linear classifier to learn a filter based on classifier weights. A hyperbolic filter is trained and optimized by doing a grid search over a set of hyperbola parameters. The PLSDA filter is generated by aligning GPR data and training PLSDA weights on that feature space. The correlation between each filter and the 2D GPR data provides information regarding the presence of buried threats. The PLSDA and hyperbolic filters were generated for a data set containing multiple target types. Both PLSDA and hyperbolic filters outperformed a prescreener for target subsets, and performed similarly over all target types. Relative to one another, both PLSDA and hyperbolic filters performed equally well. PLSDA filters, however, can be trained much faster than the corresponding exhaustive search needed by the hyperbolic filter.

Paper Details

Date Published: 29 May 2014
PDF: 10 pages
Proc. SPIE 9072, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX, 90720U (29 May 2014); doi: 10.1117/12.2050502
Show Author Affiliations
Dmitry Kalika, Duke Univ. (United States)
Kenneth D. Morton Jr., 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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