Designing an Optimal GNSS Receiver Network for Space Weather Studies Using Unsupervised Machine Learning Algorithm

Ionospheric maps from an optimal GNSS (global navigation satellite system) receiver network may lead to better understanding and prediction of the equatorial ionospheric gradients. An unsupervised machine learning framework is proposed using the k $k$‐means clustering approach to optimize the availa...

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Vydané v:	Radio science Ročník 57; číslo 12
Hlavní autori:	Dashora, N., Harsha, P. Babu Sree
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Washington Blackwell Publishing Ltd 01.12.2022
Predmet:	Algorithms Annual variations Clustering clustering algorithm Confidence limits Global navigation satellite system GNSS Histograms ionospheric maps Machine learning NavIC Optimization Receivers Satellite configurations Space weather total electron content Unsupervised learning unsupervised machine learning
ISSN:	0048-6604, 1944-799X
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