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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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
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Abstract	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.
AbstractList	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. Abstract 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.
ArticleNumber	18
Author	Ji, Hongyuan Jiang, Yiping Wei, Wenqiang Li, Wang
Author_xml	– sequence: 1 givenname: Wang surname: Li fullname: Li, Wang organization: Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University – sequence: 2 givenname: Yiping orcidid: 0000-0001-8175-9198 surname: Jiang fullname: Jiang, Yiping email: yiping.jiang@polyu.edu.hk organization: Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University – sequence: 3 givenname: Hongyuan surname: Ji fullname: Ji, Hongyuan organization: Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University – sequence: 4 givenname: Wenqiang surname: Wei fullname: Wei, Wenqiang organization: The School of Electronics and Communication Engineering, Sun Yat-sen University
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Snippet	The amplitude scintillation detection is typically achieved by using the scintillation index generated by dedicated and costly ionospheric scintillation... Abstract The amplitude scintillation detection is typically achieved by using the scintillation index generated by dedicated and costly ionospheric...
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StartPage	18
SubjectTerms	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
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Title	Amplitude scintillation detection with geodetic GNSS receivers leveraging machine learning decision tree
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