This paper proposes a methodology for automatic, accurate and early detection of amplitude ionospheric scintillation events, based on machine learning algorithms, applied on big sets of 50 Hz post-correlation data provided by a GNSS receiver. Experimental results on real data show that this approach can considerably improve traditional methods, reaching a detection accuracy of 98%, very close to human-driven manual classification. Moreover, the detection responsiveness is enhanced, enabling early scintillation alerts.
Detection of GNSS Ionospheric Scintillations based on Machine Learning Decision Tree / Linty, Nicola; Farasin, Alessandro; Favenza, Alfredo; Dovis, Fabio. - In: IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS. - ISSN 0018-9251. - ELETTRONICO. - 55:1(2019), pp. 303-317. [10.1109/TAES.2018.2850385]
Detection of GNSS Ionospheric Scintillations based on Machine Learning Decision Tree
Linty, Nicola;Farasin, Alessandro;Dovis, Fabio
2019
Abstract
This paper proposes a methodology for automatic, accurate and early detection of amplitude ionospheric scintillation events, based on machine learning algorithms, applied on big sets of 50 Hz post-correlation data provided by a GNSS receiver. Experimental results on real data show that this approach can considerably improve traditional methods, reaching a detection accuracy of 98%, very close to human-driven manual classification. Moreover, the detection responsiveness is enhanced, enabling early scintillation alerts.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2712391
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