A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation
This work addresses a computational bottleneck for radar deployment in marine atmospheric conditions, though it appears incremental as it applies existing deep learning techniques to a specific domain.
The paper tackled the problem of estimating the pattern propagation factor for radar in marine environments by developing deep neural networks that predict it from refractivity data, showing they can reasonably predict across multiple frequencies as an alternative to traditional methods.
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.