| 高频GNSS信号去噪的小波和多方向主成分分析 |
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| 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. |
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| 作者姓名： | 李保金 李艳艳 |
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| 作者单位： | 山东科技大学 测绘科学与工程学院，山东 青岛 266590 |
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| 基金项目： | 山东省自然科学基金（ZR2019BD006）；国家自然科学基金（41804001） |
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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 |
| 收稿时间： | 2021-02-06 |
Combined method of wavelet and MPCA for high-rate GNSS signal denoising |
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| Institution: | School of Surveying and Mapping Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China |
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| 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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