探地雷达层析图像标准化的杂波分布
Clutter distributions for tomographic image standardization in ground-penetrating radar
摘要：
多基地探地雷达（GPR）信号可以进行层析成像，以产生图像强度的三维分布。在没有感兴趣的对象的情况下，这些强度可以被认为是杂波的估计。这些杂波强度在空间上在几个数量级上变化，并且在不同的阵列上变化，这使得难以直接比较这些原始强度。然而，通过收集关于这些强度及其空间变化的统计数据，可以确定各种度量。在这项研究中，杂波分布被发现更好地适合一个两参数威布尔分布比高斯或对数正态分布。基于尺度和形状这两个威布尔参数的空间变化，可以从这些数据中收集更多的信息。GPR阵列在深度和交叉轨迹上照射地面的各个部分的程度可以从威布尔尺度参数的空间变化来确定，威布尔尺度参数又可以用于估计土壤中的有效衰减系数。从杂波限制条件到噪声限制条件（这是GPR穿透深度的一种可能定义）的深度过渡可以从威布尔形状参数的空间变化来估计。最后，潜在的杂波分布还提供了一个机会，标准化图像强度，以确定何时发生了统计上显着的偏离背景（杂波），这是方便的掩埋的威胁检测算法的开发，需要在多个不同的阵列是强大的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
