Real-time characterization of refractivity within the marine atmospheric boundary layer can provide valuable information that can potentially be used to mitigate the effects of atmospheric ducting on radar performance. Many duct characterization models are successful at predicting parameters from a specific refractivity profile associated with a given type of duct; however, the ability to classify, and then subsequently characterize, various duct types is an important step towards a more comprehensive prediction model. We introduce a two-step approach using deep learning to differentiate sparsely sampled propagation factor measurements collected under evaporation ducting conditions with those collected under surface-based ducting conditions in order to subsequently estimate the appropriate refractivity parameters based on that differentiation. We show that this approach is not only accurate, but also efficient; thus providing a suitable method for real-time applications.

海洋大气边界层内折射率的实时表征可以提供有价值的信息，这些信息有可能用于减轻大气导管对雷达性能的影响。许多管道表征模型成功地根据与给定类型管道相关的特定折射率分布预测参数；然而，对各种管道类型进行分类和随后表征的能力是迈向更全面的预测模型的重要一步。我们引入了一种使用深度学习的两步方法来区分在蒸发管道条件下收集的稀疏采样传播因子测量值与在基于表面的管道条件下收集的那些测量值，以便随后基于该差异估计适当的折射率参数。我们表明这种方法不仅准确，而且有效；从而为实时应用提供了一种合适的方法。