Ionospheric Echo Detection in Digital Ionograms Using Convolutional Neural Networks
Ionospheric Echo Detection in Digital Ionograms Using Convolutional Neural Networks
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DOI：
10.1029/2020rs007258
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发表时间：
2021-08-01
期刊：
影响因子：
1.6
通讯作者：
Olivares, C.
中科院分区：
文献类型：
--
作者：
De la Jara, C.;Olivares, C.
电离图是垂直传播的电波返回地球所需时间随频率变化的曲线图。时间通常表示为虚拟高度，即时间除以光速。电离图的形状是通过对发射波的频率做这个高度的轨迹来形成的。电离层回波沿着的是，电离图通常包含大量不同性质的噪声和干扰，必须将其去除，以便提取有用的信息。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提取电离层回波的数字电离图。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，电离层参数可以确定和电子密度剖面可以推导出。
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.