We study the backscattered echoes from selected targets that are extracted by an impulse radar system playing the role of a ground penetrating radar (GPR). The targets are metal and nonmetal objects buried to a selected depth in dry sand in an indoor sandbox. The recorded time-series data are analyzed in the joint time-frequency domain using a pseudo-Wigner distribution (PWD). These distributions with their extracted features in the 2D time-frequency domain are viewed as the target signatures. For target identification, a signature representation should display a 'sufficient' amount of distinguishing features, yet be robust enough to suppress the interference of noise contained in the received signals. Multiple scattering between a target and the surface of the ground is another obstacle for successful target recognition that time-frequency distributions could counteract by unveiling the time progression of the returned target information. We have previously demonstrated the merits of the PWD relative to various competing time-frequency distributions. We have also used a classification method developed from the fuzzy C-means clustering technique to reduce the number and kind of features in the PWD signature templates. This classification method has been further developed by associating a weight with each point in the cluster representation. We put the classification algorithm to a test against validation data taken from an additional set of returned echoes. The same targets are used but they are buried at a different location in the sand. Class membership of a target is then decided using a simple metric.
Time-frequency signatures based on a fuzzy-cluster representation as a means for automatic classification of targets buried underground
1998
9 Seiten, 13 Quellen
Conference paper
English
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