Clutter distributions for tomographic image standardization in ground-penetrating radar

TL;DR

This paper models ground-penetrating radar clutter intensities using Weibull distributions, enabling improved image standardization and better understanding of GPR penetration and array illumination.

Contribution

It introduces Weibull distribution fitting for GPR clutter, providing new metrics for array illumination, soil attenuation, and depth estimation, enhancing image analysis and threat detection.

Findings

Weibull distribution fits clutter better than Gaussian or lognormal.

Spatial Weibull parameters reveal array illumination and soil attenuation.

Clutter distribution modeling aids in standardizing images for threat detection.

Abstract

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.