A high-precision medium-long term prediction method for navigation satellite clock bias

被引：0
|
作者：
Yu Y. [1 ,2 ]
Huang M. [1 ,2 ,3 ]
Yang B. [1 ,2 ]
Hu R. [1 ]
Zhang F. [4 ]
机构：
[1] Institute of Microelectronics of Chinese Academy of Sciences, Beijing
[2] University of Chinese Academy of Sciences, Beijing
[3] School of Microelectronics, University of Chinese Academy of Sciences, Beijing
[4] School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2019年 / 40卷 / 09期
关键词：
Modified exponential curve method; Random error; Satellite clock bias prediction; Systematic error; Vondrak filter smoothing method;
D O I：
10.19650/j.cnki.cjsi.J1905250
中图分类号：
学科分类号：
摘要：
To improve the accuracy of medium-long term prediction of satellite clock bias (SCB), a prediction method based on Vondrak filter first-order differential modified exponential curve model (VD-MECM) is proposed. First, the frequent hopping and gross errors phenomenon of SCB before modeling are considers. The median absolute deviation is used to detect and eliminate the clock hopping and gross errors data. Meanwhile, the missing clock data can be recovered by using the Lagrange interpolation method. Secondly, the systematic and random errors of SCB are studied. Vondrak filter smoothing algorithm is used to reduce these errors. Thirdly, the prediction performance of the model is improved by considering effective data bits in SCB. The first-order difference is used to eliminate the influence of the trend item of the clock bias sequence. And, MECM prediction model is formulated. Finally, the medium-long term forecast experiments for the next four time periods are implemented based on the post-accuracy precision SCB published in the IGS server. Two typical changing trends are also considered. Experimental results show that the medium-long term prediction accuracy of this method is better than the quadratic polynomial model (QPM) and the gray model (GM (1, 1)). Compared with these two methods, the average prediction accuracy (RMS) of 60-day is increased by 92.00% and 80.80%, and the average prediction stability (Range) of 60-day is increased by 92.40% and 81.40%. © 2019, Science Press. All right reserved.
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页码：36 / 43
页数：7
相关论文
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