Abstract
In order to meet the time-critical and high-precision positioning demands of massive market applications, real-time Precise Point Positioning (PPP) technology has been continuously developed and can achieve a high level of accuracy now. Beyond precision, it is also crucial to ensure the security and stability of real-time high-precision positioning services. Nevertheless, there are few studies that addressed the fault detection and exclusion (FDE) of real-time PPP correction service to protect users from potential faults in the GNSS satellites. This paper introduces a new real-time products quality monitoring method that could effectively identify the potential fault of GNSS satellite corrections using an unsupervised learning algorithm and provide means to alert the real-time PPP users. The ambiguity-fixed Un-differenced Carrier-phase Residual Statistics (UCRS) of large-scale regional stations are first constructed to reflect the status of satellite corrections accurately. A machine learning technique, known as Isolation Forest, is employed to identify outliers in the UCRS to detect situations of potential satellite faults. Then, the UCRS alarm factors are transmitted to users for PPP processing with a modified weighting scheme based on alert information. Experimental validation utilizing 30 monitoring stations in China demonstrates a detection success rate exceeding 95% for orbit faults larger than 5 cm and clock faults larger than 0.2 ns. It is also proved that this method can effectively identify orbit and clock jump in real-time GNSS products that cause additional positioning errors. With the alert information broadcasted by the server, the PPP after FDE (PPP-FDE) presents a significant accuracy improvement of 27–71% compared with traditional PPP processing.
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Data availability
The datasets supporting this research are available from the corresponding author for academic purposes on reasonable request.
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Acknowledgements
The algorithm implementation is based on the GNSS + REsearch, Application and Teaching (GREAT) software developed by the GREAT Group, School of Geodesy and Geomatics, Wuhan University. The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.
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This work has been supported by the National Natural Science Foundation of China (No. 41974027, 42204017) and the special fund of Hubei Luojia Laboratory (220100006).
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XXL, DL, and XL provided the initial idea and designed the experiments for this study; XXL, DL, and XL analyzed the data and wrote the manuscript; and JH, JW, and HG helped with the writing. All authors reviewed the manuscript.
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Li, X., Liang, D., Li, X. et al. Quality monitoring of real-time PPP service using isolation forest-based residual anomaly detection. GPS Solut 28, 118 (2024). https://doi.org/10.1007/s10291-024-01657-z
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DOI: https://doi.org/10.1007/s10291-024-01657-z