Abstract
Aiming at the problem that the fitting sequence of the traditional gray system forecasting model can not reflect the dynamic change of the stepwise ratio of modeling data sequence, proposed to target the stepwise ratio sequence of the sequence of modeling data, a discrete gray model that reflects the trend of the modeling data sequence level is established, the specific steps of the method applied to satellite clock error prediction are given. Firstly, a corresponding step ratio sequence is generated for the modeling clock sequence; then, the step ratio sequence is modeled and predicted by the discrete gray model; finally, the relationship between the stepwise ratio and the modeled clock stepwise ratio sequence is used. The forecast results are restored to obtain the corresponding clock ratio prediction value. In order to avoid the influence of the initial deviation, the reliability of the prediction model is improved by fitting the last data of the last five epochs in the modeling data. The post-precision clock data provided by iGMAS was used for single-day and continuous multi-day forecasting experiments, and compared with the polynomial model and the traditional gray model forecast results. The results show that in the continuous multi-day forecasting experiment, the average accuracy of the forecast products obtained by the method is higher than the quadratic polynomial model, GEO satellite, IGSO satellite and MEO satellite increased by 59.45%, 37.79% and 47.60%, compared to the traditional gray model, increased 59.51%, 44.60%, and 48.46%, respectively.
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Thanks for the product support provided by the iGMAS data center.
Funding
National Key Plan (2016YFC0802206-3); National Natural Fund projects (No. 41874009, 41476087).
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Mei, C., Yang, Y., Qing, C., Xia, L., Pan, X. (2020). Discrete GM (1,1) Based on Sequence of Stepwise Ratio in the Application of the BDS Satellite Clock Bias Prediction. In: Sun, J., Yang, C., Xie, J. (eds) China Satellite Navigation Conference (CSNC) 2020 Proceedings: Volume II. CSNC 2020. Lecture Notes in Electrical Engineering, vol 651. Springer, Singapore. https://doi.org/10.1007/978-981-15-3711-0_48
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DOI: https://doi.org/10.1007/978-981-15-3711-0_48
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