标题:Middle and long-term prediction of UT1-UTC based on combination of Gray Model and Autoregressive Integrated Moving Average
作者:Jia, Song; Xu, Tian-he; Sun, Zhang-zhen; Li, Jia-jing
作者机构:[Jia, Song; Li, Jia-jing] Chang An Univ, Sch Geol Engn & Surveying, Xian 710054, Shanxi, Peoples R China.; [Xu, Tian-he; Sun, Zhang-zhen] Shandong U 更多
通讯作者:Xu, Tianhe
通讯作者地址:[Xu, TH]Shandong Univ, Inst Space Sci, Weihai 246209, Shandong, Peoples R China.
来源:ADVANCES IN SPACE RESEARCH
出版年:2017
卷:59
期:3
页码:888-894
DOI:10.1016/j.asr.2016.05.044
关键词:UT1-UTC; Prediction; GM(1,1); ARIMA
摘要:UT1-UTC is an important part of the Earth Orientation Parameters (EOP). The high-precision predictions of UT1-UTC play a key role in practical applications of deep space exploration, spacecraft tracking and satellite navigation and positioning. In this paper, a new prediction method with combination of Gray Model (GM(1, 1)) and Autoregressive Integrated Moving Average (ARIMA) is developed. The main idea is as following. Firstly, the UT1-UTC data are preprocessed by removing the leap second and Earth's zonal harmonic tidal to get UT1R-TAI data. Periodic terms are estimated and removed by the least square to get UT2R-TAI. Then the linear terms of UT2R-TAI data are modeled by the GM(1, 1), and the residual terms are modeled by the ARIMA. Finally, the UT2R-TAI prediction can be performed based on the combined model of GM(1, 1) and ARIMA, and the UT1-UTC predictions are obtained by adding the corresponding periodic terms, leap second correction and the Earth's zonal harmonic tidal correction. The results show that the proposed model can be used to predict UT1-UTC effectively with higher middle and long-term (from 32 to 360 days) accuracy than those of LS + AR, LS + MAR and WLS + MAR. (C) 2016 COSPAR. Published by Elsevier Ltd. All rights reserved.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:1
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006835784&doi=10.1016%2fj.asr.2016.05.044&partnerID=40&md5=a2785165fb4cd51affe9aed2e1bc40be
TOP