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Erschienen in:
2021 | OriginalPaper | Buchkapitel
An Algorithm for Satellite Power Anomaly Detection Based on Time Series Prediction
verfasst von : Yibo Si, Yuan Gao, Huaang Chen
Erschienen in: China Satellite Navigation Conference (CSNC 2021) Proceedings
Verlag: Springer Singapore
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Abstract
With the popularity of mobile terminals and various location-based service applications, people’s demands for location information are increasing day by day. As an infrastructure, GNSS plays a pivotal role in both military and civilian usage. Real-time monitoring of GNSS services and early warning of abnormalities are essential. In this paper, by processing the GNSS satellite observation data monitored by the IGS tracking stations, a method for judging satellite power anomalies based on time series prediction is proposed. This method can effectively solve the problems of high false and false negative alarming rate in the traditional alarming mechanisms based on the ratio comparing and the sliding window methods, and it is difficult to deal with unknown abnormalities due to excessive reliance on manual experience to set the thresholds. Experiments show that this method can effectively find GPS satellite power abnormalities, and it can be applied to other scenarios.