We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Ionospheric Echo Detection in Digital Ionograms Using Convolutional Neural Networks.
- Authors
De La Jara, C.; Olivares, C.
- Abstract
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. Key Points: This is the first time a multilayer convolutional neural network has been used to identify ionogram tracesThe trace extraction performance of the neural network surpasses the performance of some machine learning models. A comparison is presentedThe NN presented does not need a large amount of unlabeled data, good performance is achieved using a small number of labeled data
- Subjects
IONOGRAMS; IONOSPHERE; CONVOLUTIONAL neural networks; MACHINE learning; ELECTRON density
- Publication
Radio Science, 2021, Vol 56, Issue 8, p1
- ISSN
0048-6604
- Publication type
Article
- DOI
10.1029/2020RS007258