Independent Component Analysis for GPR based Hand Held Mine Detection

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.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Ground Penetrating Radar (GPR); Independent Component Analysis (ICA); Landmine-detection; Principal Component Analysis (PCA)

International Standard Serial Number (ISSN)

0277-786X

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2024 Society of Photo-optical Instrumentation Engineers, All rights reserved.

Publication Date

01 Jan 2002

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