| 联合LMD与EMD的GNSS站坐标时间序列去噪方法 |
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| 引用本文： | 熊常亮,贺小星,马下平,付杰.联合LMD与EMD的GNSS站坐标时间序列去噪方法[J].测绘通报,2022,0(2):78-82. |
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| 作者姓名： | 熊常亮 贺小星 马下平 付杰 |
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| 作者单位： | 1. 中国科学院精密测量科学与技术创新研究院, 湖北 武汉 430077;2. 中国科学院大学, 北京 100049;3. 江西理工大学土木与测绘工程学院, 江西 赣州 341000;4. 西安科技大学测绘科学与技术学院, 陕西 西安 710054;5. 华东交通大学土木建筑学院, 江西 南昌 330013 |
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| 基金项目： | 国家自然科学基金(42061077;41904171)；;江西省自然科学基金(20202BAB21402)；;江西省教育厅科学技术研究项目(GJJ200639;GJJ171293)；;江西省教育科学规划课题(21YB072)； |
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| 摘 要： | 针对GNSS站坐标时间序列信噪不易分离的问题，在传统EMD去噪方法的基础上，本文提出了一种联合LMD与EMD的坐标时间序列去噪方法。该方法首先采用LMD分解原始坐标时间序列，基于连续均方误差（CMSE）原则分离高频噪声与低频信号，保持低频分量不变；然后对高频分量进行EMD去噪；最后以2次分解所得低频信号之和作为去噪后时间序列。以仿真数据与8个GNSS基准站实测数据进行试验，通过多种评价指标进行精度评估。结果表明，与传统EMD方法相比，联合LMD与EMD的方法能够更加精确地去除坐标时间序列中的噪声。
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| 关 键 词： | 全球卫星导航系统 坐标时间序列 局部均值分解 经验模态分解 去噪分析 |
| 收稿时间： | 2021-02-28 |
| 修稿时间： | 2021-05-21 |
Denoising method of GNSS station coordinate time series based on LMD and EMD |
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| XIONG Changliang,HE Xiaoxing,MA Xiaping,FU Jie.Denoising method of GNSS station coordinate time series based on LMD and EMD[J].Bulletin of Surveying and Mapping,2022,0(2):78-82. |
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| Authors: | XIONG Changliang HE Xiaoxing MA Xiaping FU Jie |
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| Abstract: | Aiming at the problem that the signal and noise of GNSS station coordinate time series is hard to separate, based on the traditional EMD denoising method, a new method of coordinate time series denoising combining LMD and EMD is proposed. This method first uses LMD to decompose the original coordinate time series, separates high-frequency noise and low-frequency signals based on the principle of consecutive mean square error(CMSE), keeps the low-frequency components unchanged, and then performs EMD denoising on the high-frequency components. Finally, the sum of the low-frequency signals obtained by the two decompositions is the time series after denoising. Experiments are carried out with simulation data and measured data from 8 GNSS reference stations, and the accuracy is evaluated through a variety of evaluation indicators. The results show that the combined LMD and EMD method can remove the noise in coordinate time series more accurately than the traditional EMD method. |
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| Keywords: | global navigation satellite system coordinate time series local mean decomposition empirical mode decomposition denoising analysis |
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