A fuzzy-cluster representation of time-frequency signatures as a means for classification of targets buried underground

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 non-metal objects buried in dry sand to a selected depth. These echoes are studied in the joint time-frequency domain using a pseudo-Wigner distribution (PWD), which, in particular, makes it possible to analyze how each one of each target's signature features evolves in time. These distributions are viewed as the target signatures, and they are then used as templates for target classification. To be useful for target identification purposes, 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. A classification method based on a fuzzy cluster estimation technique (the fuzzy C-means algorithm) is then used to reduce the number and kind of features in the templates. 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 illuminated with the GPR antennas at different positions. 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.