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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., 12 pages, 11 figures

An adaptive automatic threat recognition system (AATR) developed at the Lawrence Livermore National Laboratory (LLNL) is described for x-ray CT images of baggage. The AATR automatically adapts to the input object requirement specification (ORS), which can change or evolve over time. These specifications characterize materials of interest (MOIs), basic physical features of interest (FOIs) (such a mass and thickness) and performance goals (detection and false alarm probability) for objects of interest (OOIs). The need and technical requirements for an AATR were developed in collaboration with DHS’s Explosives Division and Northeastern University’s Awareness and Localization of Explosives-Related Threats (ALERT) Center, a DHS Center of Excellence (http://www.northeastern.edu/alert/). Independent of the input ORS, LLNL’s AATR always uses the same algorithm and codes to process CT images. The algorithm adapts in real-time to changes in the input ORS. LLNL’s AATR is thus suitable for dynamic scenarios in which the nature of the OOIs can change rapidly. The AATR uses a spatial consensus relaxation method to determine the most likely material composition for each CT image voxel. The resulting image of most likely material compositions is segmented. An OOI classification statistic (OOI score) is computed for each voxel and each extracted image volume. OOI recognition performance is reported using various metrics on a test set of ~180 plastic bins supplied by the ALERT Center of Excellence. A method is then proposed for automatic decision threshold estimation that can adapt to the detection performance goal, the most likely material composition, and the contents of the baggage.