基于EMD的拉曼光谱去噪方法研究

经验模态分解(EMD)方法是一个以信号极值特征尺度为度量的时空滤波过程,它充分保留了信号本身的非线性和非平稳特征,在信号的滤波和去噪中具有较大的优势。文章在介绍EMD分解方法的基础上,结合EMD的多尺度滤波特性,提出了一种新的拉曼光谱去噪方法——EMD阈值去噪法。该方法首先对含噪的拉曼光谱信号做EMD分解,得到各阶本征模态函数(IMF),然后对高频的IMF分量用阈值法进行处理,把经过阈值处理后的高频IMF分量与低频IMF分量叠加得到重构的信号,即去噪信号。通过处理对二甲苯的拉曼光谱信号,分析了在不同噪声水平上不同去噪方法的处理效果。实验结果表明EMD阈值去噪法有效地去除了噪声,较好地保留了光谱的细节信息,与小波阈值去噪方法相比较具有自适应的优势,在拉曼光谱去噪中有很好的应用前景。     

脉冲星信号的经验模态分解模态单元比例萎缩消噪算法

针对脉冲星信号的消噪问题, 提出了一种基于模态单元比例萎缩的经验模态分解(EMD)消噪方法. 利用经验模态分解将含噪脉冲星信号分解为一组内蕴模态函数(IMF), 将IMF中两个过零点间的部分定义为模态单元, 以模态单元为基本单位构造最优比例萎缩因子, 对IMF中的每个模态单元进行比例萎缩去噪, 进而建立基于模态单元比例萎缩的脉冲星信号滤波模型.对含噪脉冲星信号进行了消噪实验分析, 实验结果表明, 与小波硬阈值消噪法、比例萎缩小波消噪法和基于模态单元阈值的EMD消噪法相比, 该方法可以更有效地去除脉冲星信号中的噪声, 同时更好地保留了原信号中的有用细节信息. 关键词： 经验模态分解 脉冲星信号 模态单元比例萎缩 消噪     

噪声模态单元预判的经验模态分解脉冲星信号消噪

为了改善脉冲星辐射脉冲信号的消噪效果, 提出了一种基于噪声模态单元预判的经验模态分解(EMD) 消噪声方法. 该方法首先利用EMD将含噪辐射脉冲信号分解为一组内蕴模态函数(IMF), 根据IMF系数的统计特性采用局部均方误差准则进行噪声模态单元预判, 并将噪声模态单元置零; 然后对噪声模态单元预判处理后的IMF以模态单元为基本单位进行最优比例萎缩消噪, 从而达到抑制噪声、保留信号的目的. 实验结果表明: 与Sure Shrink小波阈值法、Bayes Shrink小波阈值法和EMD模态单元比例萎缩法相比, 基于噪声模态单元预判的EMD消噪方法可以更有效地去除脉冲辐射信号中的噪声, 同时更好地保留信号突变处的细节信息特征, 在信噪比、 均方误差、峰值相对误差、峰位误差和相位误差等方面都有一定程度的改善. 关键词： 脉冲星信号消噪 经验模态分解 噪声模态单元预判 局部均方误差     

EEMD自适应去噪在拉曼光谱中的应用

二代小波是公认较好的降噪手段,但是降噪效果依赖于基函数、分解层数和阈值等参数设置。经验模态分解(empirical mode decomposition, EMD)无需参数设定,按照频率特性将信号分解成本征模函数(intrinsic mode function, IMF),对IMF滤波,实现了信号自适应去噪。拉曼光谱中信号和噪声交叠集中在极高频段,EMD产生模态混叠问题,影响去噪效果。应用总体平均经验模态分解(ensemble empirical mode decomposition,EEMD)拉曼光谱克服了模态混叠,有效区分出高频信号和噪声,获得了与小波函数相似去噪效果。文中首先对一段非线性非平稳豆油脂拉曼光谱EMD分解,可见模态混叠,EEMD分解出清晰模态的特征分量。然后分别用快速傅里叶变换(fast Fourier transform,FFT)、小波变换(Wavelet)、EMD和EEMD处理含噪光谱,信噪比、均方根误差、相关系数三个方面指标表明FFT高频去噪效果最差,其次是EMD,恰当的Wavelet同EEMD效果相当,EEMD的优势是降噪过程的自适应。最后提出光谱时频分析方法和IMF噪声属性判别准则研究趋势。     

基于EMD-LWT的低浓度石油类污染物荧光光谱去噪法EI北大核心CSCD

石油类污染物是造成雾霾等空气污染问题的重要原因.去噪处理的有效性是石油类污染物荧光光谱检测中的热点问题.提出一种基于经验模态分解-提升小波变换(EMD-LWT)相结合的低浓度石油类污染物荧光光谱去噪方法.经验模态分解法(EMD)可自适应地滤除微弱荧光信号中的噪声,但去噪过程中第一个本征模态函数(IMF)包含的频率范围过宽,影响了去噪准确性和有效性.引入提升小波变换(LWT)对IMF1实现更精细的分解,有效分离出IMF1的有用信息,改善信噪分离效果.将EMD-LWT联用方法和传统的EMD或LWT去噪法分别运用于煤油荧光光谱检测中,仿真结果表明,与只用EMD或LWT相比,EMD-LWT相结合的光谱去噪法得到的信噪比和均方根误差均显著提高,验证了该方法的有效性和可行性.     

基于经验模态分解和小波阈值的冲击信号去噪

冲击信号是非线性的并且容易受到噪声污染。为研究冲击信号去噪的问题,本文针对经验模态分解(Empirical Mode Decomposition,EMD)去噪和小波阈值去噪方法存在的不足,提出了基于EMD的小波阈值去噪方法。单纯的EMD去噪方法会在去除高频噪声的同时压制高频的有效信息。本文将EMD与小波阈值去噪相结合,利用连续均方误差准则确定含噪较多的高频固有模态函数(Intrinsic Mode Function, IMF),对高频IMF分量进行小波阈值去噪,以分离并保留这些分量中的有效信息,同时保持低频IMF分量不变。对模拟数据和实际冲击信号进行去噪处理,结果表明,基于EMD的小波阈值去噪方法的去噪效果优于单纯的EMD去噪方法和小波阈值去噪方法。     

一种改进的EMD硬阈值去噪算法

为了提升基于经典小波阈值的EMD去噪算法的性能,利用高斯白噪声的统计特征提出了一种改进的硬阈值去噪算法;首先将含噪信号进行EMD分解,把第一个固有模态函数作为高频噪声直接去除并估算出其他 IMF中高斯白噪声的能量,然后根据硬阈值去噪的原理,利用滤除掉的样本点包含的能量等于白噪声的能量确定出合适的阈值;该方法能根据样本点自适应地确定阈值;最后通过对含噪正弦信号和仿真心电信号的去噪实验证实了改进后的阈值使算法去噪效果有明显提升。     

基于EMD-LWT的低浓度石油类污染物荧光光谱去噪法

石油类污染物是造成雾霾等空气污染问题的重要原因.去噪处理的有效性是石油类污染物荧光光谱检测中的热点问题.提出一种基于经验模态分解-提升小波变换(EMD-LWT)相结合的低浓度石油类污染物荧光光谱去噪方法.经验模态分解法(EMD)可自适应地滤除微弱荧光信号中的噪声,但去噪过程中第一个本征模态函数(IMF)包含的频率范围过宽,影响了去噪准确性和有效性.引入提升小波变换(LWT)对IMF1实现更精细的分解,有效分离出IMF1的有用信息,改善信噪分离效果.将EMD-LWT联用方法和传统的EMD或LWT去噪法分别运用于煤油荧光光谱检测中,仿真结果表明,与只用EMD或LWT相比,EMD-LWT相结合的光谱去噪法得到的信噪比和均方根误差均显著提高,验证了该方法的有效性和可行性.     

CEEMDAN联合小波阈值算法在水下激光雷达中抑制散射杂波的应用

水下激光雷达回波信号中往往含有大量散射噪声.为了能够有效抑制散射噪声,提高水下激光雷达测距精度,提出了基于自适应完备噪声经验模态分解(CEEMDAN)与小波阈值相结合的去噪新方法.首先通过相关系数法对自适应完备噪声经验模态分解得到的本征模态函数(IMF)进行筛选;然后对筛选后的本征模态函数进行小波阈值去噪,进一步去除本征模态函数中的噪声成分;最后将去噪后的本征模态函数进行信号重构得到去噪后信号.将该方法应用到不同衰减系数水体的强度调制连续光水下测距实验,使用白色聚氯乙烯(PVC)反射板为探测目标,在3.75个衰减长度时,直接采用相关极值确定延时,测距误差达到19.2 cm;应用该方法处理后,测距误差减小到6.2 cm,有效提高测距精度.     

基于噪声辅助非均匀采样复数据经验模态分解的混沌信号降噪

鉴于非均匀采样复数据经验模态分解(NSBEMD)相对传统分解方法的优势和噪声的NSBEMD特性,提出了一种基于噪声辅助NSBEMD的混沌信号自适应降噪方法.该方法首先以含噪混沌信号和高斯白噪声分别为实、虚部来构造复数据并进行NSBEMD,然后根据虚部各IMF的能量来估算实部各IMF中包含的噪声能量,最后根据噪声能量的估计值对实部IMF进行奇异值分解(SVD)降噪.噪声估计实验验证了噪声能量估计方法的可行性,而Lorenz信号和太阳黑子月平均数的降噪实验则表明,相对于现有EMD降噪方法,本文方法能够进一步消除噪声,更清晰地恢复出混沌吸引子的拓扑结构.     

Relevant modes selection method based on Spearman correlation coefficient for laser signal denoising using empirical mode decomposition

Empirical mode decomposition (EMD) is a recently proposed nonlinear and nonstationary laser signal denoising method. A noisy signal is broken down using EMD into oscillatory components that are called intrinsic mode functions (IMFs). Thresholding-based denoising and correlation-based partial reconstruction of IMFs are the two main research directions for EMD-based denoising. Similar to other decomposition-based denoising approaches, EMD-based denoising methods require a reliable threshold to determine which IMFs are noise components and which IMFs are noise-free components. In this work, we propose a new approach in which each IMF is first denoised using EMD interval thresholding (EMD-IT), and then a robust thresholding process based on Spearman correlation coefficient is used for relevant modes selection. The proposed method tackles the problem using a thresholding-based denoising approach coupled with partial reconstruction of the relevant IMFs. Other traditional denoising methods, including correlation-based EMD partial reconstruction (EMD-Correlation), discrete Fourier transform and wavelet-based methods, are investigated to provide a comparison with the proposed technique. Simulation and test results demonstrate the superior performance of the proposed method when compared with the other methods.     

利用经验模态分解的高频水声信号滤波方法*

研究了一种高频水声信号的滤波问题,提出了一种改进的经验模态分解加小波阈值滤波方法。首先对信号进行带通滤波处理,再进行经验模态分解,将分解得到的各个模态转换为频域信号,采用小波软阈值方法对这些频域信号进行滤波,最后对信号进行重构,并转换为时域信号。经数值仿真与试验数据验证表明此方法是可行有效的,与原基于经验模态分解的小波阈值滤波方法相比,本方法滤波效果较好:对不同输入信噪比的仿真信号进行滤波后,本方法的输出信噪比最大提高17.41 d B,滤波后所得信号与加噪前纯信号的相关系数最大提高0.90;对实验数据进行滤波后,不同时间段信号的相关系数最大提高0.62。     

Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition

This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length K. Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into K number of Band Limited IMFs (BLIMFs). The BLIMFs are separated as noise, noise-dominant, and signal-dominant BLIMFs by calculating the MI between BLIMFs. Eventually, the noise BLIMFs are discarded from further processing, noise-dominant BLIMFs are denoised using GSTV, and the signal BLIMFs are added to reconstruct the output signal. The regularization parameter λ for GSTV is automatically selected based on the values of Dispersion Entropy of the noise-dominant BLIMFs. The effectiveness of the proposed denoising method is evaluated in terms of performance metrics such as Signal-to-Noise Ratio, Root Mean Square Error, and Correlation Coefficient, which are are compared to EMD variants, and the results demonstrated that the proposed approach is able to effectively denoise the synthetic Blocks, Bumps, Doppler, Heavy Sine, PD pulses and real PD signals.     

A Novel Noise Reduction Method of UAV Magnetic Survey Data Based on CEEMDAN,Permutation Entropy,Correlation Coefficient and Wavelet Threshold Denoising

Despite the increased attention that has been given to the unmanned aerial vehicle (UAV)-based magnetic survey systems in the past decade, the processing of UAV magnetic data is still a tough task. In this paper, we propose a novel noise reduction method of UAV magnetic data based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy (PE), correlation coefficient and wavelet threshold denoising. The original signal is first decomposed into several intrinsic mode functions (IMFs) by CEEMDAN, and the PE of each IMF is calculated. Second, IMFs are divided into four categories according to the quartiles of PE, namely, noise IMFs, noise-dominant IMFs, signal-dominant IMFs, and signal IMFs. Then the noise IMFs are removed, and correlation coefficients are used to identify the real signal-dominant IMFs. Finally, the wavelet threshold denoising is applied to the real signal-dominant IMFs, the denoised signal can be obtained by combining the signal IMFs and the denoised IMFs. Both synthetic and field experiments are conducted to verify the effectiveness of the proposed method. The results show that the proposed method can eliminate the interference to a great extent, which lays a foundation for the further interpretation of UAV magnetic data.     

基于经验模式分解的拖曳式声纳拖船噪声抵消研究

拖曳式线列阵声纳的拖船噪声具有多途角扩展等特点，并且是一个非平稳过程，使得对该噪声的消除或抑制是一大难点。经验模式分解是一种用于分析非线性非平稳信号的新方法，该方法自适应地将嵌于数据内部的多个固有模式函数逐一分解开来。本文尝试利用经验模式分解方法分离出水听器接收信号中的拖船干扰噪声，从而达到消除干扰的目的。海上试验数据的处理结果充分验证了这种方法的可行性。     

SINS初始对准中光纤陀螺EMD滤波

为了实现低成本SINS初始对准,降低对准过程复杂程度,提高系统对准精度,缩短对准时间,本文引入了EMD滤波技术。首先,采集IMU输出信号,根据EMD算法将信号分解为IMF簇,按照CMSE标准对信号进行重构,完成信号滤波处理;接着,按照AR模型对经EMD滤波前后的数据噪声进行建模;然后,分别利用原始信号和EMD降噪后信号进行SINS姿态粗对准;最后,根据IMU模型和SINS误差模型,采用零速对准方式,完成SINS精对准。实验结果表明:经EMD降噪后的信号粗对准精度为1.3°,精对准精度为0.87 mrad,精对准收敛时间为200 s。     

改进的经验模态分解法分离超声多普勒血流与管壁信号

超声多普勒血流信号常包含管壁信号的干扰,准确分离二者对提高血流检测的精度具有重要作用。本文提出两种改进的经验模态分解(EMD)方法,先将含管壁信号的超声多普勒信号分解成多层本征模态函数(IMF),然后根据血流信号与管壁信号的不同特性,对既含管壁信号又含血流信号的IMF分量进行分离处理,最后将各层IMF分量中的管壁成分叠加得到管壁信号的估计,而血流信号可通过原信号减去估计的管壁信号而得到。将本方法用于计算机仿真信号和人体实测的超声多普勒信号,并与高通滤波器法、空间选择性降噪法和原EMD法进行比较,结果表明:本文提出的两种方法能在较大的管壁搏动速度范围内准确地分离血流信号和管壁信号,其平均相对误差比高通滤波器的结果降低了约52%和57%。可见,本文提出的两种方法有望用于血流信号与管壁信号的准确分离。      

Comparison of simultaneous signals obtained from a dual-field-of-view lidar and its application to noise reduction based on empirical mode decomposition

Although the empirical mode decomposition (EMD) method is an effective tool for noise reduction in lidar signals, evaluating the effectiveness of the denoising method is difficult. A dual-field-of-view lidar for observing atmospheric aerosols is described. The backscattering signals obtained from two channels have different signal-to-noise ratios (SNRs). The performance of noise reduction can be investigated by comparing the high SNR signal and the denoised low SNR signal without a simulation experiment. With this approach, the signal and noise are extracted to one intrinsic mode function (IMF) by the EMD- based denoising; thus, the threshold method is applied to the IMFs. Experimental results show that the improved threshold method can effectively perform noise reduction while preserving useful sudden-change information.     

一种改进的奇异值降噪阶次选取方法用于紫外光谱信号去噪的研究

光谱去噪是光谱检测的重要环节。针对光谱信号易受光谱仪热噪声、现场机械振动以及随机噪声等因素影响,而在线监测系统要求减少人为参数选择对去噪效果的影响,提出利用奇异值分解(SVD)理论对光谱信号去噪。提出一种改进的降噪阶次选取方法：指定奇异值差分谱最大峰值点θ₁为所选阶次下界;利用奇异值、奇异值差分谱综合信息选取阶次上界θ₂;将区间θ₁～θ₂定义为模糊区域,通过模糊C均值聚类求取隶属度,赋予模糊区域内奇异值相应的权重系数。用所提方法对不同信噪比下SO₂紫外光谱信号去噪,将信噪比、均方根误差、波形相似系数、平滑度指标用于去噪效果的评价。去噪结果表明：所提方法完全基于数据驱动,具有较好的去噪效果,能够真实的恢复原始信号。     

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

In this paper, a new method to reduce noises within chaotic signals based on ICA(independent component analysis)and EMD(empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions(IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals.Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.