引用本文: 黄博华, 杨勃航, 李锡瑞, 朱祥维, 王宇谱. 顾及一次差分数据结构特征的钟差预报模型[J]. 武汉大学学报 ( 信息科学版), 2021, 46(8): 1161-1169.
HUANG Bohua, YANG Bohang, LI Xirui, ZHU Xiangwei, WANG Yupu. Prediction of Navigation Satellite Clock Bias Considering Structure Characteristics of Single Difference Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1161-1169.
 Citation: HUANG Bohua, YANG Bohang, LI Xirui, ZHU Xiangwei, WANG Yupu. Prediction of Navigation Satellite Clock Bias Considering Structure Characteristics of Single Difference Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1161-1169.

## Prediction of Navigation Satellite Clock Bias Considering Structure Characteristics of Single Difference Data

• 摘要: 现有卫星钟差预报模型缺乏对数据结构特征的深入研究。以钟差一次差分数据为研究对象，分析一次差分数据结构的图形化分布模式，提取一次差分数据趋势性和周期性特征，设计了一种包含趋势项、周期项和随机项的全要素钟差预报模型。使用IGS(International GNSS Service)精密钟差数据进行预报实验，通过与二次多项式模型、灰色模型及时间序列模型的预报结果进行对比，证明了所提模型在钟差预报的准确度和稳定度方面具有一定优势。

Abstract:
Objectives  Prediction of navigation satellite clock bias (SCB) plays an important role in maintaining system time synchronization and optimizing SCB parameters in navigation messages. SCB is a nonlinear and non-stationary complex random sequence, which is difficult to make accurate prediction with a single mathematical model. Besides, the traditional prediction models of SCB lack of the in-depth research of data structure characteristics.
Methods  With the single difference of SCB as the research object, we first analyzed the graphical distribution pattern of the single difference structure. Then, by extracting the trend and cyclic of the single difference, a SCB prediction model including trend part, cyclic part and random part was proposed.
Results  We used International GNSS Service (IGS) precision SCB as reference for prediction experiments. Based on the root mean squared error between the prediction result and the IGS SCB, we analyzed the accuracy and stability of the SCB prediction between different methods. The mean error was taken as the index to evaluate the accuracy of the SCB prediction from the perspective of satellite. The confidence interval of the prediction result distribution was used to evaluate the stability of the SCB prediction. Compared to quadratic polynomial model (QP), grey model(1, 1) (GM (1, 1)), and autoregressive integrated moving average model (ARIMA), the prediction quality of the method was superior to that of the other three methods.
Conclusions  The proposed method avoids the disadvantage of the traditional model in prediction quality and algorithm complexity by constantly optimizing the parameters, and effectively improves the accuracy and stability of the SCB prediction.

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