一种高精度导航卫星钟差中长期预报方法
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1中国科学院微电子研究所北京100029; 2中国科学院大学北京100049; 3中国科学院大学微电子学院北京100049; 4郑州轻工业大学计算机与通信工程学院郑州450001;

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TH762

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A highprecision mediumlong term prediction method
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1.Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China; 2.University of Chinese Academy of Sciences, Beijing 100049, China; 3.School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China; 4.School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China

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    摘要:

    为了提高卫星钟差中长期预报的精度,提出了一种基于冯德拉克滤波一次差的修正指数曲线法模型的卫星钟差中长期预报方法。该方法首先在建模之前考虑到卫星钟差钟跳和粗差频繁的现象,采用中位数法探测钟跳和粗差数据并将其剔除后,采用拉格朗日插值法将缺失的钟差数据补齐;其次,考虑到卫星钟差数据存在系统误差和随机误差,采用冯德拉克滤波平滑法对钟差数据进行平滑处理;然后,考虑到卫星钟差的有效数字位数较多,会降低模型的预报性能,采用一次差处理消除钟差序列趋势项的影响后,建立了修正指数曲线法预报模型;最后,采用IGS服务器上发布的事后精密卫星钟差产品,并结合2种典型变化趋势的卫星钟差进行了未来4个时间段的中长期预报实验。实验结果表明,该方法的中长期预报性能明显优于常用的二次多项式模型和灰色模型,其60 d的平均预报精度(RMS)相对于常用的二次多项式模型和灰色模型分别提高了9200%和8080%,平均预报稳定度(Range)相对于常用的二次多项式模型和灰色模型分别提高了9240%和8140%。

    Abstract:

    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%.

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于烨,黄默,杨斌,胡锐,张飞燕.一种高精度导航卫星钟差中长期预报方法[J].仪器仪表学报,2019,40(9):36-43

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  • 在线发布日期: 2020-08-20
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