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Ionospheric Echo Detection in Digital Ionograms Using Convolutional Neural Networks
Radio Science ( IF 1.6 ) Pub Date : 2021-07-26 , DOI: 10.1029/2020rs007258
C. De La Jara 1 , C. Olivares 2
Affiliation  

An ionogram is a graph of the time that a vertically transmitted wave takes to return to the earth as a function of frequency. Time is typically represented as virtual height, which is the time divided by the speed of light. The ionogram is shaped by making a trace of this height against the frequency of the transmitted wave. Along with the echoes of the ionosphere, ionograms usually contain a large amount of noise and interference of different nature that must be removed in order to extract useful information. In the present work, we propose a method based on convolutional neural networks to extract ionospheric echoes from digital ionograms. Extraction using the CNN model is compared with extraction using machine learning techniques. From the extracted traces, ionospheric parameters can be determined and electron density profile can be derived.

中文翻译:

使用卷积神经网络在数字电离图中进行电离层回波检测

电离图是垂直传播的波返回地球所需的时间与频率的函数关系图。时间通常表示为虚拟高度,即时间除以光速。电离图的形状是根据传输波的频率绘制此高度的轨迹。与电离层的回波一起,电离图通常包含大量不同性质的噪声和干扰,为了提取有用的信息,必须去除这些噪声和干扰。在目前的工作中,我们提出了一种基于卷积神经网络的方法来从数字电离图中提取电离层回波。将使用 CNN 模型的提取与使用机器学习技术的提取进行比较。从提取的轨迹中,可以确定电离层参数并推导出电子密度分布。
更新日期:2021-08-13
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