Abstract
In order to effectively identify the abnormal data in the GPS (Global Positioning System) monitoring data, the method of the CUSUM (Cumulative Sum) median control chart was introduced. Aiming at the problem that the traditional mean control graph cannot accurately identify the outliers in the actual sample data, a GPS anomaly data recognition algorithm based on the CUSUM median control chart was proposed, and the basic principles and calculation steps were given. On the basis, considering the influence of non-normal data in the calculation of the algorithm, a method of converting to normal data was given. Finally, the feasibility and effectiveness of the proposed method were verified by simulation data. The experimental results show that the proposed algorithm has a good effect. Compared with the traditional CUSUM control chart, the abnormal value recognition ability was improved, and the false alarm rate was also effectively controlled.
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Acknowledgments
The project was financially supported by the National Natural Science Foundation of China (no. 41404004).
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Wu, H., Li, M., Liu, C. (2019). An Outlier Recognition Method Based on Improved CUSUM for GPS Time Series. In: Xie, Y., Zhang, A., Liu, H., Feng, L. (eds) Geo-informatics in Sustainable Ecosystem and Society. GSES 2018. Communications in Computer and Information Science, vol 980. Springer, Singapore. https://doi.org/10.1007/978-981-13-7025-0_42
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DOI: https://doi.org/10.1007/978-981-13-7025-0_42
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