电子学报 ›› 2019, Vol. 47 ›› Issue (7): 1416-1424.DOI: 10.3969/j.issn.0372-2112.2019.07.004

• 学术论文 • 上一篇    下一篇

基于灰色模型和混沌时间序列的卫星钟差预测算法

黄飞江1,2, 陈演羽1,2, 李廷会2, 袁海波3, 单庆晓1   

  1. 1. 长沙学院电子信息与电气工程学院, 湖南长沙 410022;
    2. 广西师范大学电子工程学院, 广西桂林 541004;
    3. 中国科学院国家授时中心, 陕西西安 710600
  • 收稿日期:2018-04-17 修回日期:2019-01-10 出版日期:2019-07-25 发布日期:2019-07-25
  • 通讯作者: 李廷会
  • 作者简介:黄飞江 男,1972年9月出生,湖南汝城人.2003年和2009年分别在广西师范大学和中国科学院研究生院获工学硕士学位和理学博士学位.现为长沙学院教授,主要研究方向为时间同步、卫星导航和星间链路.E-mail:ccsuhfj@163.com;陈演羽 男,1988年11月出生,广西梧州人.2014年于天津商业大学获得工学学士学位.现为广西师范大学电子工程学院硕士研究生,主要研究方向为卫星导航与钟差预测.E-mail:chyy966@126.com
  • 基金资助:
    国家自然科学基金(No.61264008,No.11773030,No.11373075);湖南省自然科学基金(No.2015JJ2016);长沙学院"青年英才支持计划"和科研基金(No.SF1615)

A Satellite Clock Bias Prediction Algorithm Based on Grey Model and Chaotic Time Series

HUANG Fei-jiang1,2, CHEN Yan-yu1,2, LI Ting-hui2, YUAN Hai-bo3, SHAN Qing-xiao1   

  1. 1. College of Electronic Information and Electrical Engineering, Changsha University, Changsha, Hunan 410022, China;
    2. College of Electronic Engineering, Guangxi Normal University, Guilin, Guangxi 541004, China;
    3. National Time Service Center, Chinese Academy of Sciences, Xi'an, Shaanxi 710600, China
  • Received:2018-04-17 Revised:2019-01-10 Online:2019-07-25 Published:2019-07-25

摘要: 为了提高非线性卫星钟差预测的精度,降低单一钟差预测模型对钟差预测的风险,提出了一种组合模型的卫星钟差预测算法.该算法首先采用db1小波对卫星钟差序列进行3层多分辨率分解和单支重构,得到一个趋势分量和三个细节分量,然后运用灰色预测模型对重构后的趋势分量和混沌一阶加权局域预测法对重构后的细节分量分别进行预测,最后将各分量预测结果相加后得到总的钟差预测值.以GPS卫星钟差数据做算例分析,在6小时的钟差预测中,算法绝对误差最大值比单一的灰色预测模型误差小1.3ns以上.将该组合预测模型用于非线性卫星钟差预测中,可以提高钟差预测的精度和可靠性.

关键词: 卫星钟差, 小波分解, 灰色模型, 混沌时间序列, 一阶加权局域预测

Abstract: In order to improve the accuracy of nonlinear satellite clock bias prediction and reduce the risk of single clock bias prediction model for clock bias prediction,a satellite clock bias prediction algorithm based on combined model is proposed.The algorithm firstly uses db1 wavelet to conduct a 3 layer multiresolution decomposition and single branch reconstruction of satellite clock bias sequences,and obtains a trend component and three detail components.Then the grey prediction model is used to predict the reconstructed trend component and the chaotic time series one-order weighted local prediction method is used to predict the reconstructed detail components.Finally,each component prediction result is added to obtain the total clock bias prediction value.Taking the GPS satellite clock bias data as an example,the maximum absolute error of the algorithm is at least 1.3ns smaller than that of a single gray prediction model in 6-hour clock bias prediction.This combined prediction model can be applied to the prediction of nonlinear satellite clock bias,which can improve the accuracy and reliability of clock bias prediction.

Key words: satellite clock bias, wavelet decomposition, grey model, chaotic time series, one-order weighted local prediction

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