用深度学习快速估算海面大气层多频段信号传播因子
A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation
- 用图像到图像的神经网络，从修正折射率数据预测传播因子分布
- 模型在多频段下可合理预测传播因子，速度远超传统方法
- 适合雷达部署优化与实时环境建模场景
准确估计海面大气边界层内多频段的折射环境，对雷达技术的有效部署至关重要。传统抛物方程模拟虽有效，但计算成本高、耗时长，限制了实际应用。本文探索一种基于深度神经网络的新方法，用于估计表征环境对信号传播影响的关键参数——模式传播因子。设计了图像到图像的翻译生成器，输入经修改的修正折射率数据，输出相同域内的传播因子预测结果。实验表明，深度神经网络可被训练以分析多频段数据，并合理预测模式传播因子，为传统方法提供高效替代方案。
原文摘要 · Abstract (English)
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.
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