Automatic detection of ionospheric scintillation‐like GNSS satellite oscillator anomaly using a machine‐learning algorithm. Issue 3 (24th August 2020)

• Record Type: Journal Article
• Authors: Liu, Yunxiang; Morton, Y. Jade
• Is Part Of: Navigation. Volume 67:Issue 3(2020)
• Page Start: 651
• Page End: 662
• Publication Date: 2020-08-24
• Subjects: global monitoring system -- kernel trick -- machine learning -- phase scintillation -- satellite oscillator anomaly -- support vector machine; Navigation -- Periodicals; Navigation (Aeronautics) -- Periodicals; Navigatie; Navigation -- Périodiques; Navigation (Aéronautique) -- Périodiques; NAVIGATION; MARITIME SATELLITES; NAVIGATION SATELLITES; 527.05
• DOI: 10.1002/navi.385
• Languages: English
• ISSNs: 0028-1522

Abstract

Abstract: In this paper, we propose a machine‐learning‐based approach to automatically detect a 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 to an extensive GNSS database to conduct automatic satellite oscillator anomaly detection. Preliminary detection results validate the effectiveness of the proposed method. On average, one‐to‐three satellite oscillator anomaly events are detected daily at each receiver location.