A Real-Time Anomaly Monitoring Method based on GLRT for Satellite Clock Offsets

Lin Zhao, Nan Li, Hui Li, Xue Liu, Wenzhen Peng

Peer Reviewed

Abstract:	Real-time satellite clock offset anomaly monitoring is of great significance for real-time precise point positioning (RT-PPP) applications. Gross errors and phase jumps are the common anomalies in real-time satellite clock offsets. To detect these anomalies quickly and accurately, a real-time anomaly monitoring method based on generalized likelihood ratio test (GLRT) and adaptive Kalman filter (AKF) is proposed. The innovation sequence of AKF is used to reflect the abnormal variation of satellite clock offsets. By adjusting the adaptive factors of AKF, a reliable innovation sequence can be obtained. The GLRT is used as a detector to detect anomalies in satellite clock offsets with the aid of innovation sequence. By comparing the likelihood ratio test statistics with a priori detection threshold, the anomalies can be detected in real time. Based on the different performance of anomalies in the innovation sequence, the gross error and phase jump can be distinguished. Numerical simulations are employed to demonstrate the effectiveness of AKF in resisting the influences of phase jumps. The performance of the proposed anomaly monitoring method is evaluated, and the results show that the monitoring method can provide a quickly detection and identification of satellite clock offset anomalies and has a low probability of false alarm (PFA) and good probability of detection (PD).
Published in:	Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022) September 19 - 23, 2022 Hyatt Regency Denver Denver, Colorado
Pages:	1555 - 1566
Cite this article:	Zhao, Lin, Li, Nan, Li, Hui, Liu, Xue, Peng, Wenzhen, "A Real-Time Anomaly Monitoring Method based on GLRT for Satellite Clock Offsets," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 1555-1566. https://doi.org/10.33012/2022.18427
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