| EEMD-多尺度排列熵的GPS高程时间序列降噪方法 |
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| 引用本文： | 鲁铁定,谢建雄.EEMD-多尺度排列熵的GPS高程时间序列降噪方法[J].大地测量与地球动力学,2021,41(2):111-115. |
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| 作者姓名： | 鲁铁定 谢建雄 |
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| 作者单位： | 东华理工大学测绘工程学院,南昌市广兰大道418号,330013;东华理工大学江西省数字国土重点实验室,南昌市广兰大道418号,330013;东华理工大学测绘工程学院,南昌市广兰大道418号,330013;漳州市测绘设计研究院,福建省漳州市龙溪南路5-58号,363000 |
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| 基金项目： | 国家重点研发计划;江西省自然科学基金;国家自然科学基金 |
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| 摘 要： | 针对GPS高程时间序列受各类噪声干扰的影响，导致难以提取有用信息的问题，提出一种基于整体经验模态分解(EEMD)结合多尺度排列熵(MPE)的阈值降噪方法。该方法以EEMD为核心算法，将原始信号分解成一系列本征模态函数(IMF)，并采用MPE作为指标将其分类为噪声IMF、混合IMF和信息IMF；然后利用阈值函数处理混合IMF，实现二次降噪；再重构降噪后的数据与信息IMF，获得降噪结果。仿真信号和实例分析结果表明，该方法与相关系数法、MPE法相比，降噪评价指标RMSE、SNR和dnSNR均为最优，说明该降噪方法效果最好，本文方法获得的降噪结果能够更好地反映出时间序列本身的非线性变化特性，可为GPS高程时间序列分析提供可靠依据。
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| 关 键 词： | 经验模态分解 多尺度排列熵 GPS高程时间序列 阈值降噪 |
EEMD-Multiscale Permutation Entropy Noise Reduction Method for GPS Elevation Time Series |
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| LU Tieding,XIE Jianxiong.EEMD-Multiscale Permutation Entropy Noise Reduction Method for GPS Elevation Time Series[J].Journal of Geodesy and Geodynamics,2021,41(2):111-115. |
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| Authors: | LU Tieding XIE Jianxiong |
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| Institution: | (Faculty of Geomatics,East China University of Technology,418 Guanglan Road,Nanchang 330013,China;Key Laboratory for Digital Land and Resources of Jiangxi Province,East China University of Technology 418 Guanglan Road,Nanchang 330013,China;Zhangzhou Institute of Surveying and Mapping,5-58 South-Longxi Road,Zhangzhou 363000,China) |
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| Abstract: | Aiming at the problem that GPS elevation time series is affected by various types of noise,which makes it difficult to extract useful information,we propose a threshold denoising method based on ensemble empirical mode decomposition(EEMD)and multi-scale permutation entropy(MPE).The method uses EEMD as the core algorithm.EEMD can decompose the original signal into a series of intrinsic modal function(IMF),use MPE as an indicator to classify it into noise IMF,hybrid IMF and information IMF,and then use threshold function to process the mixed IMF to achieve secondary noise reduction.Finally,the noise-reduced data and information IMF are reconstructed to obtain noise reduction results.By analyzing the simulation signals and examples,the results show that compared with the correlation coefficient method and the MPE method,the noise reduction evaluation indexes RMSE,SNR,and dnSNR are all optimal,indicating that the new method has the best noise reduction effect.It further demonstrates that the noise reduction results obtained by the new method can better reflect the nonlinear variation characteristics of the time series itself,and can provide a reliable basis for GPS elevation time series analysis. |
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| Keywords: | empirical mode decomposition(EMD) multi-scale permutation entropy(MPE) GPS elevation time series threshold denoising |
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