测绘通报 ›› 2023, Vol. 0 ›› Issue (4): 163-166,171.doi: 10.13474/j.cnki.11-2246.2023.0123

• 技术交流 • 上一篇    下一篇

海洋平台GNSS RTK监测数据的CVCEEMDAN-WT-SSA去噪算法

熊春宝, 张子健, 陈雯, 于丽娜   

  1. 天津大学建筑工程学院, 天津 300072
  • 收稿日期:2022-07-01 发布日期:2023-04-25
  • 通讯作者: 张子健。E-mail:zijian_zhang@tju.edu.cn
  • 作者简介:熊春宝(1964—),男,博士,教授,研究方向为工程结构健康监测。E-mail:luhai-tj@126.com
  • 基金资助:
    国家自然科学基金(61971037)

CVCEEMDAN-WT-SSA algorithm of denoising the data from monitoring offshore platform by GNSS RTK

XIONG Chunbao, ZHANG Zijian, CHEN Wen, YU Lina   

  1. School of Civil Engineering, Tianjin University, Tianjin 300072, China
  • Received:2022-07-01 Published:2023-04-25

摘要: 针对GNSS-RTK技术在海洋平台变形位移监测过程中的多路径效应误差与随机噪声,本文提出一种基于交叉证认改进的具有自适应白噪声的完整集成经验模态分解(CVCEEMDAN)、小波阈值(WT)降噪方法及奇异谱分析(SSA)相结合的联合去噪算法。首先对原始信号进行CEEMDAN分解,使用交叉证认方法识别噪声与有效信号IMF分量;然后利用WT和SSA分别对噪声和有效信号分量作去噪处理,重构处理后的信号,获得真实变形监测结果。结果表明:本文算法具有自适应性,且相比EMD、EEMD、CEEMDAN、ACCEEMDAN-WT-SSA算法具有更好的去噪效果,可有效去除海洋平台变形监测中的多路径误差及随机噪声,成功获取真实的监测信号结果。

关键词: GNSS-RTK, 海洋平台, 交叉证认, 奇异谱分析, 小波阈值去噪

Abstract: Aiming at the multi-path errors and random noise of GNSS RTK technology in the deformation and displacement monitoring of offshore platform, a combined denoising algorithm based on cross-validation improvement of complete integrated empirical mode decomposition (CVCEEMDAN), wavelet threshold (WT) noise reduction method and singular spectrum analysis (SSA). Firstly, the original signal is decomposed by CEEMDAN, and the IMF components of noise and effective signal are identified by cross-validation method. Then WT and SSA are used to denoise the noise and effective signal components respectively, and the processed signal is reconstructed to obtain the real deformation monitoring results. The results show that this algorithm is adaptive and has better denoising effect than EMD, EEMD, CEEMDAN and ACCEEMDAN-WT-SSA algorithms. It can effectively remove multi-path errors and random noise in the deformation monitoring of offshore platform, and successfully obtain the real monitoring signal results.

Key words: GNSS RTK, offshore platform, cross-validation, singular spectrum analysis, wavelet threshold denoising

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