Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning

Abstract

This paper presents a method for predicting atmospheric duct conditions from a clutter power spectrum using deep learning. To accurately predict the duct conditions, deep learning with a binary classification is applied to the proposed refractivity from the clutter (RFC) method. The input data set is the artificial clutter data that are generated via the Advanced Refractive Prediction System (AREPS) simulation software Ver. 3.6 in conjunction with random atmospheric refractive indices. The output of the RFC method is then predicted via binary classification, indicating whether the atmospheric conditions are duct or non-duct. For the cross-validation, the clutter power spectrum data are generated based on real atmospheric refractivity data. The results show that the DNN trained with 5600 pieces of data (validation accuracy of 95.99%) exhibits a binary classification accuracy of 98.36%. The deep neural network (DNN) trained with 28,000 pieces of data (validation accuracy of 98.20%) achieves a binary classification accuracy of 99.06% with an F1-score of 0.9921.

Keywords

• radio propagation
• radar
• deep learning
• RFC
• binary classification
• clutter power