|Abstract:||In this paper, we propose a machine learning-based approach to automatically detect satellite oscillator anomaly. A major challenge is to differentiate an oscillator anomaly from ionospheric scintillation. Although both scintillation and oscillator anomalies cause phase disturbances, their underlying physics are different and, therefore, show different carrier frequency dependency. By using triple-frequency signals, distinct features are extracted from the disturbed signals and applied to the radial basis function (RBF) support vector machine (SVM) classifier to identify an oscillator anomaly. The results show that the proposed RBF SVM displays superior performance and outperforms several other classification methods. The proposed approach is applied on a database to conduct automatic satellite oscillator anomaly detection. Preliminary detection results show that there are on average 1.7 and 0.2 anomaly events observed on PRN1 and PRN25 each day, respectively.|
Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)
September 16 - 20, 2019
Hyatt Regency Miami
|Pages:||3390 - 3400|
|Cite this article:||
Liu, Yunxiang, Morton, Y.T. Jade, "Automatic Detection of Ionospheric Scintillationlike GNSS Oscillator Anomaly Using a Machine Learning Algorithm," Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, Florida, September 2019, pp. 3390-3400.
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