| 基于CEEMDAN的GNSS变形监测去噪方法 |
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| 引用本文： | 汤俊,李垠健,高鑫.基于CEEMDAN的GNSS变形监测去噪方法[J].大地测量与地球动力学,2021,41(4):408-413. |
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| 作者姓名： | 汤俊 李垠健 高鑫 |
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| 作者单位： | 华东交通大学土木建筑学院,南昌市双港东大街808号,330013;华东交通大学土木工程国家实验教学示范中心,南昌市双港东大街808号,330013 |
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| 基金项目： | 国家自然科学基金;江西省教育厅科技项目;江西省研究生创新专项资金 |
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| 摘 要： | 为了提高GNSS变形监测数据去噪的有效性和可靠性，提出一种自适应完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN)去噪方法。首先将GNSS变形序列经CEEMDAN分解为若干特征模态函数；其次引入排列熵理论确定高低频分界值K，利用小波分析对高频分量进行去噪，去噪后与低频分量重构得到去噪序列；最后通过仿真和实测边坡GNSS变形监测数据，利用信噪比、均方根误差、相关系数等指标对比分析CEEMDAN、EMD和小波去噪方法。结果表明，CEEMDAN方法的去噪效果和精度优于EMD和小波去噪方法，证明了本文所提方法的有效性和可靠性。
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| 关 键 词： | GNSS变形监测 CEEMDAN 排列熵 去噪方法 |
GNSS Deformation Monitoring Denoising Method Based on CEEMDAN |
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| TANG Jun,LI Yinjian,GAO Xin.GNSS Deformation Monitoring Denoising Method Based on CEEMDAN[J].Journal of Geodesy and Geodynamics,2021,41(4):408-413. |
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| Authors: | TANG Jun LI Yinjian GAO Xin |
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| Institution: | (School of Civil Engineering and Architecture,East China Jiaotong University,808 East-Shuanggang Street,Nanchang 330013,China;National Experimental Teaching Demonstration Center of Civil Engineering,East China Jiaotong University,808 East-Shuanggang Street,Nanchang 330013,China) |
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| Abstract: | In order to improve the effectiveness and reliability of noise reduction from GNSS deformation monitoring data,we propose the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method.First,the GNSS deformation sequence is decomposed into several characteristic modal functions by CEEMDAN.Second,we introduce the permutation entropy theory to determine the high and low frequency boundary value K,then use wavelet analysis to denoise the high frequency component.We reconstruct the denoising sequence with the low frequency component after denoising.Finally,through simulation data and measured slope GNSS deformation monitoring data,we compare and analyze CEEMDAN,EMD and wavelet denoising methods using signal-to-noise ratio,root mean square error,correlation coefficient and other indicators.The results show that CEEMDAN is superior to EMD and wavelet denoising methods,proving the effectiveness and reliability of the method proposed in this paper. |
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| Keywords: | GNSS deformation monitoring CEEMDAN permutation entropy denoising method |
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