Combined method of wavelet and MPCA for high-rate GNSS signal denoising
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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.
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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
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. -
表 1 滤波前后 10 个GNSS 站的残差时间序列的平均中误差
mm 方向 GNSS EMD-PCA WD-PCA WD-MPCA E 4.40 3.06 2.86 0.83 N 4.70 3.15 2.99 0.85 U 13.92 9.10 8.50 8.30 
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