| 局部均值分解和奇异值分解在GNSS站坐标时间序列信号降噪中的应用 |
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| 引用本文： | 邱小梦,王奉伟,周世健,邹时林.局部均值分解和奇异值分解在GNSS站坐标时间序列信号降噪中的应用[J].测绘通报,2020,0(5):85-89. |
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| 作者姓名： | 邱小梦 王奉伟 周世健 邹时林 |
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| 作者单位： | 1. 东华理工大学长江学院, 江西 抚州 334000;2. 同济大学测绘与地理信息学院, 上海 200092;3. 南昌航空大学, 江西 南昌 330063 |
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| 基金项目： | 江西省教育厅科技项目（181523）；东华理工大学长江学院院长基金 |
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| 摘 要： | 为了有效地提取GNSS站坐标时间序列的有用信息,降低噪声干扰,本文提出一种局部均值分解和奇异值分解相结合的信号降噪方法,并利用5个测站的实测坐标时间序列对新方法进行了验证。首先通过局部均值分解将坐标时间序列分解成一系列PF分量和余项,然后利用连续均方误差方法确定高频分量与低频分量的分界点,保持低频分量不变,运用奇异值分解方法对高频分量进行降噪重构,最后将重构的高频分量与低频分量叠加得到最终的降噪坐标时间序列,并对降噪效果进行对比分析。结果表明,与单纯的奇异值分解方法相比,局部均值分解和奇异值分解相结合方法能够自适应地选择合适的奇异值个数进行信号重构,提高了降噪效果。
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| 关 键 词： | 局部均值分解 奇异值分解 连续均方误差 奇异值差分谱 坐标时间序列 |
| 收稿时间： | 2019-12-03 |
| 修稿时间： | 2019-12-10 |
Application of local mean decomposition and singular value decomposition in noise reduction of GNSS station coordinate time series signal |
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| QIU Xiaomeng,WANG Fengwei,ZHOU Shijian,ZOU Shilin.Application of local mean decomposition and singular value decomposition in noise reduction of GNSS station coordinate time series signal[J].Bulletin of Surveying and Mapping,2020,0(5):85-89. |
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| Authors: | QIU Xiaomeng WANG Fengwei ZHOU Shijian ZOU Shilin |
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| Institution: | 1. Yangtze River College, East China University of Technology, Fuzhou 334000, China;2. College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China;3. Nanchang Hangkong University, Nanchang 330063, China |
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| Abstract: | In order to effectively extract useful information from coordinate time series of GNSS station, and reduce noise interference, this paper proposes a signal denoising method that combining local mean decomposition with singular value decomposition. Experiments were carried out using measured coordinate time series of five stations. Firstly, the coordinate time series is decomposed into a series of PF components and residuals by local mean decomposition, and then the continuous mean square error method is used to determine the boundary between the high frequency component and the low frequency component. Keep the low-frequency components unchanged, and use the singular value decomposition method to denoise and reconstruct the high-frequency components. Finally, the reconstructed high-frequency components and low-frequency components are superimposed to obtain the final de-noising coordinate time series, and the noise reduction effect is compared and analyzed. The results show that compared with the simple singular value decomposition, the local mean decomposition combined with singular value decomposition can adaptively select the appropriate number of singular values for signal reconstruction, which improves the noise reduction effect. |
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| Keywords: | local mean decomposition singular value decomposition continuous mean square error singular value difference spectrum coordinate time series |
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