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高频GNSS信号去噪的小波和多方向主成分分析
引用本文:李保金,李艳艳. 高频GNSS信号去噪的小波和多方向主成分分析[J]. 全球定位系统, 2021, 46(4): 33-39. DOI: 10.12265/j.gnss.2021020601
作者姓名:李保金  李艳艳
作者单位:山东科技大学 测绘科学与工程学院,山东 青岛 266590
基金项目:山东省自然科学基金(ZR2019BD006);国家自然科学基金(41804001)
摘    要:针对传统主成分分析(PCA)忽视测站各坐标分量之间相关性的问题,提出了一种小波去噪和多方向主成分分析(WD-MPCA)组合的方法. 该方法弥补了传统PCA的缺陷,与经验模态分解和主成分分析(EMD-PCA)组合方法及小波去噪和主成分分析(WD-PCA)组合方法相比,WD-MPCA组合方法精度最高. 经WD-MPCA组合方法去噪后,其平均中误差分别为0.83 mm、0.85 mm和8.30 mm,比原始坐标残差时间序列的平均中误差分别降低了81.14%、81.91%和40.37%. WD-MPCA组合方法充分考虑了各测站不同分量之间的相关性,可以有效去除信号中的高频随机白噪声(WN)和低频有色噪声(CN),这对高频全球卫星导航系统(GNSS)技术的实际应用和理论发展具有重要的意义. 

关 键 词:高频全球卫星导航系统(GNSS)  经验模态分解(EMD)  小波去噪(WD)  主成分分析(PCA)  组合去噪
收稿时间:2021-02-06

Combined method of wavelet and MPCA for high-rate GNSS signal denoising
Affiliation:School of Surveying and Mapping Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Abstract:In view of the problem that traditional principal component analysis (PCA) ignores the correlation between coordinate components of stations, a method combining wavelet denoising and multi-directional principal component analysis (WD-MPCA) is proposed, which makes up for the shortcomings of traditional PCA. Compared with empirical mode decomposition and principal component analysis (EMD-PCA) and wavelet denoising and principal component analysis (WD-PCA). The WD-MPCA combination method has the highest accuracy. After denoising by WD-MPCA combination method, the mean median error is 0.83 mm, 0.85 mm and 8.30 mm respectively, which is 81.14%, 81.91% and 40.37% lower than that of the original coordinate residual time series. The WD-MPCA combination method fully considers the correlation between different components of each station. It can effectively remove high-frequency random white noise (WN) and low-frequency colored noise (CN), which is of great significance to the practical application and theoretical development of high-frequency Global Navigation Satellite System (GNSS) technology. 
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