测绘学报 ›› 2019, Vol. 48 ›› Issue (10): 1225-1235.doi: 10.11947/j.AGCS.2019.20180271

• 大地测量学与导航 • 上一篇    下一篇

AR模型中AO类异常值探测及其在GPS卫星钟差预报中的应用

韩松辉1, 张国超2, 张宁1, 朱建青3   

  1. 1. 信息工程大学基础部, 河南 郑州 450001;
    2. 中国人民解放军78092部队, 四川 成都 610000;
    3. 苏州科技大学理学院, 江苏 苏州 215009
  • 收稿日期:2018-06-12 修回日期:2019-01-01 出版日期:2019-10-20 发布日期:2019-10-24
  • 作者简介:韩松辉(1980-),男,博士,副教授,研究方向为测量数据处理。E-mail:hansonghui@126.com
  • 基金资助:
    国家自然科学基金(41474009;41774038)

New algorithm for detecting AO outliers in AR model and its application in the prediction of GPS satellite clock errors

HAN Songhui1, ZHANG Guochao2, ZHANG Ning1, ZHU Jianqing3   

  1. 1. Department of Basic, Information Engineering University, Zhengzhou 450001, China;
    2. Troops 78092, Chengdu 610000, China;
    3. College of Mathematics and Physics, Suzhou University of Science and Technology, Suzhou 215009, China
  • Received:2018-06-12 Revised:2019-01-01 Online:2019-10-20 Published:2019-10-24
  • Supported by:
    The National Natural Science Foundation of China (Nos. 41474009;41774038)

摘要: 基于EM算法,提出一种AR模型中AO类异常值(additive outlier)探测的算法。该算法可同时进行AR模型拟合与AO类异常值探测,并可有效地解决成片AO类异常值探测时所产生的掩盖和淹没问题。最后,将本文算法应用于GPS卫星钟差预报之中。本文算法可以准确探测出钟差历史观测序列中的AO类异常值,并可对卫星钟差进行精确预报。

关键词: AR模型, EM算法, AO类异常值, 卫星钟差预报

Abstract: Based on the EM algorithm, an algorithm for detecting additive outlier in an autoregressive (AR) time series is proposed. The algorithm can fit the AR model and detect the additive outlier at the same time, and it can efficiently prevent the occurrence of masking and swamping.At last, the proposed algorithm is applied to process the data of GPS satellite clock error prediction. The examples verify the effectiveness of the algorithm in detecting the additive outlier and predicting the satellite clock error.

Key words: autoregressive model, EM algorithm, AO outlier, satellite clock error

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