A novel feature extraction and buried object identification method for ground penetrating radar data are presented. Discriminative features are obtained by modelling the most dynamic peaks of GPR A-scan signals, utilizing principal component analysis (PCA). Landmine/clutter discrimination is then achieved using fuzzy k-nearest neighbor algorithm. The identification results are presented on a real data set of 700 surrogate landmines and clutter objects, which were collected from three different terrains with various soil types and buried object depths. We show that the proposed method gives outstanding results over this extensive data set.
In this study, we provide an extensive comparison of different clutter suppression techniques that are proposed
to enhance ground penetrating radar (GPR) data. Unlike previous studies, we directly measure and present
the effect of these preprocessing algorithms on the detection performance. Basic linear prediction algorithm
is selected as the detection scheme and it is applied to real GPR data after applying each of the available
clutter suppression techniques. All methods are tested on an extensive data set of different surrogate mines
and other objects that are commonly encountered under the ground. Among several algorithms, singular value
decomposition based clutter suppression stands out with its superior performance and low computational cost,
which makes it practical to use in real-time applications.
KEYWORDS: General packet radio service, Mining, Statistical analysis, Wavelets, Error analysis, Wavelet transforms, Signal detection, Discrete wavelet transforms, Signal processing, Data processing
In the underground inspection problem, signature of a big target at a certain depth may give equivalent
information to the signature of a smaller target at shallower depth, unless depth information is not used. This
results in a difficulty in the identification process. Therefore, depth information is coming into prominence
in the classification step to increase the identification performance. In this study, we propose a burial depth
estimation method on GPR data. In our work, discrete wavelet transform is used in the preprocessing step.
After this stage, statistical hypothesis tests are utilized to detect the statistical discrepancies in the returning
signals at different depth levels.
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