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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Bibliographic Details
Published in:	Radio science Vol. 57; no. 12
Main Authors:	Dashora, N., Harsha, P. Babu Sree
Format:	Journal Article
Language:	English
Published:	Washington Blackwell Publishing Ltd 01.12.2022
Subjects:	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
Online Access:	Get full text
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