Amplitude scintillation detection with geodetic GNSS receivers leveraging machine learning decision tree

The amplitude scintillation detection is typically achieved by using the scintillation index generated by dedicated and costly ionospheric scintillation monitoring receivers (ISMRs). Considering the large volume of common Global Navigation Satellite System (GNSS) receivers, this paper presents a str...

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Bibliographic Details
Published in:Satellite navigation Vol. 5; no. 1; pp. 18 - 11
Main Authors: Li, Wang, Jiang, Yiping, Ji, Hongyuan, Wei, Wenqiang
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.12.2024
Springer Nature B.V
SpringerOpen
Subjects:
Accuracy
Algorithms
Amplitude scintillation
Charged particles
Decision trees
Engineering
False alarms
GNSS Ionospheric Monitoring and Modelling
Human error
Ionosphere
Machine learning
Methods
Original Article
Satellites
Signal processing
Support vector machines
Ionosphere
Amplitude scintillation
Machine learning
ISSN:2662-9291, 2662-1363
Online Access:Get full text
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Summary:The amplitude scintillation detection is typically achieved by using the scintillation index generated by dedicated and costly ionospheric scintillation monitoring receivers (ISMRs). Considering the large volume of common Global Navigation Satellite System (GNSS) receivers, this paper presents a strategy to accurately identify the ionospheric amplitude scintillation events utilizing the measurements collected with geodetic GNSS receivers. The proposed detection method relies on a pre-trained machine learning decision tree algorithm, leveraging the scintillation index computed from the carrier-to-noise data and elevation angles collected at 1-Hz. The experimental results using real data demonstrate a 99% accuracy in scintillation detection can be achieved. By combining advanced machine learning techniques with geodetic GNSS receivers, this approach is feasible to effectively detect ionospheric scintillation using non-scintillation GNSS receivers.
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ISSN:2662-9291
2662-1363
DOI:10.1186/s43020-024-00136-7