联合交叉验证和CEEMD-WT的GNSS时间序列降噪方法

曲轩宇, 李新瑞, 郑蕾, 许豪, 舒宝, 王利

曲轩宇, 李新瑞, 郑蕾, 许豪, 舒宝, 王利. 联合交叉验证和CEEMD-WT的GNSS时间序列降噪方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220570
引用本文: 曲轩宇, 李新瑞, 郑蕾, 许豪, 舒宝, 王利. 联合交叉验证和CEEMD-WT的GNSS时间序列降噪方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220570
Qu Xuanyu, Li Xinrui, Zheng Lei, Xu Hao, Shu Bao, Wang Li. A GNSS Time Series Denoising Method with Mixed Use of Cross-Validation and CEEMD-WT[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220570
Citation: Qu Xuanyu, Li Xinrui, Zheng Lei, Xu Hao, Shu Bao, Wang Li. A GNSS Time Series Denoising Method with Mixed Use of Cross-Validation and CEEMD-WT[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220570

联合交叉验证和CEEMD-WT的GNSS时间序列降噪方法

基金项目: 

国家自然科学基金项目(41877289,42004024,42127802);国家重点研发计划项目(2021YFC3000503,2021YFC3000501)

详细信息
    作者简介:

    曲轩宇,博士生,主要从事GNSS定位、多传感器融合及结构健康监测研究。xuany.qu@connect.polyu.hk

    通讯作者:

    李新瑞,博士生。lixinrui_98@chd.edu.cn

  • 中图分类号: P228

A GNSS Time Series Denoising Method with Mixed Use of Cross-Validation and CEEMD-WT

  • Abstract:

    Objectives:  The coordinate time series derived from Global navigation satellite system (GNSS) technology usually contains noise, which may lead to misinterpretation of some geophysical phenomena. To reduce to noise level of GNSS datasets, a denoising method based on the mixed use of cross-validation (CV), complementary ensemble empirical mode decomposition (CEEMD) and wavelet transform (WT) is proposed.   Methods:  Specifically, the original GNSS time series is first decomposed into several Intrinsic Mode Function (IMF) components by CEEMD. Then, the CV strategy is applied to divide the IMFs into signal with slight noise components and pure noise components. Finally, after removing the pure noise components, wavelet denoising method is adopted to eliminate the noise in the remaining IMFs to obtain final GNSS time series.   Results:  Both simulated signals and real-world datasets collected from 117 GNSS stations were used to evaluate the performance of the proposed method. Results show that the proposed method can effectively attenuate the noise in the original time series. Compared with the residuals of the original time series, the residuals of denoising results derived from the proposed method are reduced by 43%, 43%, and 46% in the north (N), east (E), and up (U) directions, respectively.   Conclusions:  Compared with wavelet transform, CV-based CEEMD and correlation coefficient-based CEEMD methods, the proposed method can effectively reduce the noise effects in GNSS time series.

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出版历程
  • 收稿日期:  2023-06-03
  • 网络出版日期:  2023-06-27

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