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Proceedings Paper

Independent component analysis for GPR based hand held mine detection
Author(s): Partha Pratim Palit; Sanjeev Agarwal
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Paper Abstract

Unlike Vehicle-mounted ground penetrating radar (GPR), the hand-held GPR data is highly variable. In this paper we propose an independent component analysis (ICA) based approach for processing hand held stepped frequency GPR data for mine detection. ICA is a linear transformation, which seeks prominent features in high-dimensional data. Compared to principal component analysis (PCA), which searches for basis vectors in the direction of maximum variance, ICA finds more interesting features in the direction of maximum non-gaussianity. In our current implementation, ICA is used to find a set of basis vectors corresponding to the background clutter. Residual error for this GPR with respect to ICA clutter basis shows the presence or absence of landmine. The performance of the ICA based detection is compared with the correlation detector for GPR only data for hand held mine detection. Comparative receiver operating characteristics (ROC) curves representing probability of detection verses false alarm rate are shown for both scan and investigative mode for ICA based detection and correlation detection.

Paper Details

Date Published: 13 August 2002
PDF: 11 pages
Proc. SPIE 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII, (13 August 2002); doi: 10.1117/12.479109
Show Author Affiliations
Partha Pratim Palit, Univ. of Missouri/Rolla (United States)
Sanjeev Agarwal, Univ. of Missouri/Rolla (United States)


Published in SPIE Proceedings Vol. 4742:
Detection and Remediation Technologies for Mines and Minelike Targets VII
J. Thomas Broach; Russell S Harmon; Gerald J. Dobeck, Editor(s)

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