Detection of GNSS Ionospheric Scintillations Based on Machine Learning Decision Tree

Linty Nicola; Farasin Alessandro; Favenza Alfredo; Dovis Fabio

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Detection of GNSS Ionospheric Scintillations Based on Machine Learning Decision Tree

机译：基于机器学习决策树的GNSS电离层闪烁检测

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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 postcorrelation data provided by a global navigation satellite system 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.

机译：本文提出了一种基于机器学习算法的自动，准确和早期检测振幅电离层闪烁事件的方法，该方法适用于全球导航卫星系统接收器提供的大量50 Hz后相关数据。实际数据的实验结果表明，该方法可以显着改进传统方法，检测精度达到98％，非常接近于人为驱动的手动分类。此外，检测响应能力得到增强，可以提早发出闪烁警报。

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《IEEE Transactions on Aerospace and Electronic Systems》 |2019年第1期|303-317|共15页
作者
Linty Nicola; Farasin Alessandro; Favenza Alfredo; Dovis Fabio;
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作者单位

Politecn Torino, Dept Elect & Telecommun, I-10129 Turin, Italy;

Ist Super Mario Boella, I-10129 Turin, Italy|Politecn Torino, Dept Control & Comp Engn, I-10129 Turin, Italy;

Ist Super Mario Boella, I-10129 Turin, Italy;

Politecn Torino, Dept Elect & Telecommun, I-10129 Turin, Italy;

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Detection of GNSS Ionospheric Scintillations Based on Machine Learning Decision Tree

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