Fuzzy-cluster representation of time-frequency signatures as a means for automatic classification of buried mine-like targets

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 using a pseudo-Wigner distribution (PWD). These distributions with their extracted features in the two-dimensional time-frequency domain are viewed as the target signatures. We use a classification method developed from the “fuzzy C-means” clustering technique to reduce the number and kind of features in the PWD signatures. This is accomplished by converting the PWD signature into a point cluster representation where each point is associated with a weight proportional to the value of the modulus of the PWD. Using a modified fuzzy C-means technique the cluster representation is then reduced to a (smaller) set of cluster centers. 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. The results of our investigation serve to assess the possibility of identifying subsurface targets using a GPR, by means of the present technique