Assessment of statistical-based clutter reduction techniques on ground-coupled GPR data for the detection of buried objects in soils
Abstract
A bi-static Ground Penetrating Radar (GPR)
has been developed for the detection of cracks and buried
pipes in urban grounds. It is made of two shielded Ultra Wide
Band (UWB) bowtie-slot antennas operating in the frequency
band [0.3;4] GHz. GPR signals contain not only responses of
targets, but also unwanted effects from antenna coupling in
air and in the soil, system ringing, and soil reflections that can
mask the proper detection of useful information. Thus, it
appears necessary to propose and assess several clutter
reduction techniques as pre-processing techniques to improve
the signal-to-noise ratio, discriminate overlapping responses
issued from the targets and the clutter, and ease the use of
data processing algorithms for target detection, identification
or reconstruction. In this work, we have evaluated on Bscan
profiles three different statistical data analysis such as mean
subtraction, Principal Component Analysis (PCA), and
Independent Component Analysis (ICA) considering a
shallow and a medium depth target. The receiver operating
characteristics (ROC) graph has allowed to evaluate the
performance of each data processing in simulations and
measurements to further draw a comparison in order to select
the technique most adapted to a given soil structure with its
radar probing system.
Reference
@inproceedings{jpt-gpr14,
author = {Tebchrany, E. and Sagnard, F. and Baltazart, V. and Tarel, J.-P. and Derobert, X.},
title = {Assessment of statistical-based clutter reduction techniques on ground-coupled GPR data for the detection of buried objects in soils},
booktitle = {International Conference on Ground Penetrating Radar (GPR'14)},
date = {June 30-July 4},
address = {Brussels, Belgium},
year = {2014},
pages = {604-609},
note = {http://perso.lcpc.fr/tarel.jean-philippe/publis/gpr14.html}
}
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