基于CEEMD的耦合模型在GNSS变形监测数据降噪中的应用

Application of Coupling Model Based on CEEMD in Noise Reduction of GNSS Deformation Monitoring Data

为了有效提取GNSS(Global Navigation Satellite System)变形监测数据中的有用信息,最大限度降低噪声对数据后续分析的干扰,本文提出了一种基于完备集合经验模态分解(Complete Ensemble Empirical Mode Decomposition,CEEMD)的耦合降噪模型。该耦合模型实现信号降噪的步骤为:首先,使用CEEMD对原始信号进行自适应分解,得到若干个本征模态函数(Intrinsic Mode Function,IMF)以及残余项;其次,根据不同IMF分量标准化模量的累计均值(Mean of Standardized Accumulated Modes,MSAM)将IMF分量分为高频IMF分量与低频IMF分量;最后,分别使用Wavlet与GavGol滤波方法对高频分量、低频分量进一步去噪并重构降噪结果,得到最终降噪信号。使用仿真数据与实测GNSS数据对本文提出耦合模型进行检验,结果表明,相较于单一的降噪模型,本文提出模型能够更加有效地剔除噪声,表现出了更好的降噪效果。

In order to effectively extract the useful information from the deformation monitoring data of GNSS(Global Navigation Satellite System)and minimize the interference of noise on the subsequent analysis of data,a coupled noise reduction model based on complete ensemble empirical mode decomposition(CEEMD)is proposed in this paper.The steps of signal noise reduction in the coupling model are as follows:Firstly,CEEMD is used to adaptively decompose the original signal to obtain several intrinsic mode functions(IMF)and residual terms;Secondly,according to the mean of standardized accumulated modes(MSAM)of different IMF components,IMF components are divided into high-frequency IMF components and low-frequency IMF components;Finally,the Wavelet and GavGol filtering methods are used to further denoise the high-frequency components and low-frequency components,and the denoising results are reconstructed to obtain the final denoised signal.The simulation data and the measured GNSS data are used to test the coupling model proposed in this paper.The results show that compared with a single noise reduction model,the model proposed in this paper can eliminate noise more effectively and show a better noise reduction effect.