A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation
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.
Cite
@article{arxiv.2505.15802,
title = {A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation},
author = {Sarah E. Wessinger and Leslie N. Smith and Jacob Gull and Jonathan Gehman and Zachary Beever and Andrew J. Kammerer},
journal= {arXiv preprint arXiv:2505.15802},
year = {2025}
}
Comments
This work has been submitted to the IEEE for possible publication