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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.15802 |
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| _version_ | 1866908519607828480 |
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| author | Wessinger, Sarah E. Smith, Leslie N. Gull, Jacob Gehman, Jonathan Beever, Zachary Kammerer, Andrew J. |
| author_facet | Wessinger, Sarah E. Smith, Leslie N. Gull, Jacob Gehman, Jonathan Beever, Zachary Kammerer, Andrew J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15802 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Wessinger, Sarah E. Smith, Leslie N. Gull, Jacob Gehman, Jonathan Beever, Zachary Kammerer, Andrew J. Machine Learning Signal Processing Atmospheric and Oceanic Physics 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. |
| title | A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation |
| topic | Machine Learning Signal Processing Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2505.15802 |