Deep learning for classifying and characterizing atmospheric ducting within the maritime setting

Abstract

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 duct 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.