A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation

TL;DR

This paper introduces a deep learning approach using neural networks to efficiently estimate the pattern propagation factor across multiple frequencies in marine atmospheric environments, providing a faster alternative to traditional simulation methods.

Contribution

The study develops a novel deep neural network framework for multi-frequency pattern propagation factor estimation, reducing computational costs compared to conventional parabolic equation simulations.

Findings

Neural networks can predict pattern propagation factors with reasonable accuracy.

The approach handles multiple frequencies simultaneously.

Deep learning offers a computationally efficient alternative to traditional methods.

Abstract

Accurately estimating the refractive environment over multiple frequencies within the marine atmospheric boundary layer is crucial for the effective deployment of radar technologies. Traditional parabolic equation simulations, while effective, can be computationally expensive and time-intensive, limiting their practical application. This communication explores a novel approach using deep neural networks to estimate the pattern propagation factor, a critical parameter for characterizing environmental impacts on signal propagation. Image-to-image translation generators designed to ingest modified refractivity data and generate predictions of pattern propagation factors over the same domain were developed. Findings demonstrate that deep neural networks can be trained to analyze multiple frequencies and reasonably predict the pattern propagation factor, offering an alternative to traditional methods.