AI Data Classification of Atmospheric Ducts for Advanced Radar Modeling

Abstract

This study explores the development of neural network models for predicting atmospheric refractivity profiles from the input of sea clutter returns, with a focus on two distinct duct types: evaporation ducts and hybrid ducts. Accurate prediction of these profiles is critical for various defense and communication applications, where atmospheric refractivity significantly influences radar performance and signal propagation. To capture the differing characteristics of each duct type, three models were evaluated: two specialized models trained independently on evaporation and hybrid duct data, and a joint model trained on both. Prior to training, the dataset was balanced by down-sampling the more frequent hybrid duct cases to prevent bias.
Model performance was assessed using standard regression metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The specialized evaporation model achieved an R² score of 0.9894 and an RMSE of 0.10322, while the hybrid duct model demonstrated superior precision with an R² of 0.9996 and RMSE of just 0.02095. The joint model maintained strong overall performance (R² = 0.9965, RMSE = 0.0591), though slight performance degradation was noted in evaporation duct cases, suggesting mild overfitting to the structurally complex hybrid ducts. Model predictions closely matched true refractivity profiles across both duct types, including cases with steep gradient transitions. These results underscore the capacity of neural networks to generalize well across atmospheric
conditions. While joint models offer practical efficiency, specialized models may provide enhanced accuracy for operational scenarios where precision in refractivity prediction is critical.