  Multistatic ground-penetrating radar (GPR) signals can be imaged
tomographically to produce three-dimensional distributions of image
intensities. In the absence of objects of interest, these intensities can be
considered to be estimates of clutter. These clutter intensities spatially vary
over several orders of magnitude, and vary across different arrays, which makes
direct comparison of these raw intensities difficult. However, by gathering
statistics on these intensities and their spatial variation, a variety of
metrics can be determined. In this study, the clutter distribution is found to
fit better to a two-parameter Weibull distribution than Gaussian or lognormal
distributions. Based upon the spatial variation of the two Weibull parameters,
scale and shape, more information may be gleaned from these data. How well the
GPR array is illuminating various parts of the ground, in depth and
cross-track, may be determined from the spatial variation of the Weibull scale
parameter, which may in turn be used to estimate an effective attenuation
coefficient in the soil. The transition in depth from clutter-limited to
noise-limited conditions (which is one possible definition of GPR penetration
depth) can be estimated from the spatial variation of the Weibull shape
parameter. Finally, the underlying clutter distributions also provide an
opportunity to standardize image intensities to determine when a statistically
significant deviation from background (clutter) has occurred, which is
convenient for buried threat detection algorithm development which needs to be
robust across multiple different arrays.
