极点均值型经验模式分解及其去噪应用

使用经验模式分解（EMD）对信号进行去噪时,由于EMD 本身会产生模态混叠,往往很难将噪声完全分离。针对这一问题,提出了一种新型的极点均值型EMD 方法,并且给予固有模态函数（IMF）一个新的定义。首先,将相邻极点平均以求得均值包络,然后迭代相减进而获得IMF。最后用原始信号减去分离出的高频IMF 实现去噪。随机信号仿真以及激光雷达回波信号去噪实验表明,该方法与EMD 分解相比,可以更好地将噪声分离,有效地抑制模态混叠,更可以极大地减小均方误差。因此,极点均值型EMD 拥有很好前景。     

一种改进的经验模型分解方法

在对复杂信号进行分析中,常把它展开成一系列基本信号,然后,通过研究每个基本成分或者相应系数的特点来分析复杂信号。Huang等人提出经验模型分解方法(Empirical Mode Decomposition,EMD),通过筛选,将复杂信号中分解成一系列内在模型函数(Intrinsic Mode Function,IMF)。在本论文中,作者对经验模型分解中的一个重要的筛选过程作了部分改进,提出了一种改进检验模型分解法(Modified EMD,MEMD)。利用改进检验模型分解法,能够既快又准确地获得内在模型函数,而且,得到的内在模型函数能保留原信号中各成分的瞬时频率的规律。     

自适应掩膜信号集成局部特征尺度分解及其应用

局部特征尺度分解（LCD）是为克服经验模态分解（EMD）中均值曲线构造的不足而提出的一种自适应信号分解方法,已被应用于机械故障诊断领域.但LCD存在与EMD类似的模态混叠问题,为此,基于均匀相位差掩膜信号构造,提出了自适应掩膜信号集成局部特征尺度分解（AMSELCD）,该方法不仅能够将一个复杂信号自适应地分解为若干个本征模态函数和一个剩余项之和,而且能够有效地解决LCD的模态混叠现象.通过仿真信号分析,将AMSELCD与现有多种抑制模态分解方法进行了对比,结果表明了所提方法的有效性和优越性.最后,针对滚动轴承和转子碰摩故障振动信号的调制特征,将所提AMSELCD方法应用于转子碰摩和滚动轴承的故障诊断,对比和实验分析结果进一步验证了所提方法的有效性和优越性.     

The removal of wall components in Doppler ultrasound signals by using the empirical mode decomposition algorithm

Doppler ultrasound systems, used for the noninvasive detection of the vascular diseases, normally employ a high-pass filter (HPF) to remove the large, low-frequency components from the vessel wall from the blood flow signal. Unfortunately, the filter also removes the low-frequency Doppler signals arising from slow-moving blood. In this paper, we propose to use a novel technique, called the empirical mode decomposition (EMD), to remove the wall components from the mixed signals. The EMD is firstly to decompose a signal into a finite and usually small number of individual components named intrinsic mode functions (IMFs). Then a strategy based on the ratios between two adjacent values of the wall-to-blood signal ratio (WBSR) has been developed to automatically identify and remove the relevant IMFs that contribute to the wall components. This method is applied to process the simulated and clinical Doppler ultrasound signals. Compared with the results based on the traditional high-pass filter, the new approach obtains improved performance for wall components removal from the mixed signals effectively and objectively, and provides us with more accurate low blood flow.     

总体经验模态细分法提取血流超声多普勒信号的研究

提出基于总体经验模态分解（EEMD）血流细分法提高血流超声多普勒信号提取精度.首先估计辅助分析所需的白噪声幅度,进而用EEMD得到无模态混叠的本征模态函数（IMF）组,最后分离出血流信号的IMF.将本方法应用于计算机仿真和人体实测超声多普勒信号,并与高通滤波器法、原EMD法和EMD细分法比较.结果表明本文方法,提取的血流信号精度最高,特别对WBSR=70dB的混合信号,其精度比上述方法分别提高35%、38%及17%.     

Hierarchical decomposition based on a variation of empirical mode decomposition

Adaptive methods of signal analysis have proved a very useful tool for analysis of non-stationary signals. This is due to the ability of these methods to adapt to the local structures of the signals being analysed, as these methods are not constrained by a fixed basis. Empirical mode decomposition (EMD) is among the more recent data-adaptive signal decomposition methods, which decomposes a given signal into modes which are hierarchically arranged based on their frequency content. In this paper, we will present a novel adaptive hierarchical decomposition scheme based on a novel modification of EMD, namely empirical mode decomposition-modified peak selection (EMD-MPS). EMD-MPS allows a time-scale-based signal decomposition, thereby allowing control over the decomposition process, not possible in the original EMD algorithm. Using time-scale-based decomposition and the properties of EMD-MPS, a given signal can be decomposed into octave frequency bands, with the centre frequency of the separated modes given by the frequency separation criterion of EMD-MPS. The spectral limits of the separated bands are established, and their relation with the centre frequency derived empirically. The method is validated by its application to simulated and real signals.     

基于拐点尺度的对经验模态分解的改进

 对EMD在信号处理的过程中存在的部分问题展开了研究和对几篇关于EMD的文章给出的结论进行了分析,从直观上对选取拐点来作为衡量信号振荡形式的标准的合理性进行了初探,并在此基础上进一步给出了相应的算法来对EMD在处理信号时的性能进行改进,对仿真和实际信号的处理结果表明了改进后的EMD算法和拐点特征尺度的合理性和有效性;在新算法的基础上对固有模态函数(IMF)的定义进行了一定的补充.     

Gain-adapted hidden Markov models for recognition of clean andnoisy speech

In applying hidden Markov modeling for recognition of speech signals, the matching of the energy contour of the signal to the energy contour of the model for that signal is normally achieved by appropriate normalization of each vector of the signal prior to both training and recognition. This approach, however, is not applicable when only noisy signals are available for recognition. A unified approach is developed for gain adaptation in recognition of clean and noisy signals. In this approach, hidden Markov models (HMMs) for gain-normalized clean signals are designed using maximum-likelihood (ML) estimates of the gain contours of the clean training sequences. The models are combined with ML estimates of the gain contours of the clean test signals, obtained from the given clean or noisy signals, in performing recognition using the maximum a posteriori decision rule. The gain-adapted training and recognition algorithms are developed for HMMs with Gaussian subsources using the expectation-minimization (EM) approach     

一种改进的基于EMD分解的超宽带信号消噪算法

针对低信噪比超宽带信号的消噪问题,提出一种改进的基于经验模式分解(EMD)的消噪算法.该算法首先对含噪信号进行EMD分解,得到多个固有模态函数(IMF)分量,然后选取高阶IMF重构原信号,达到消噪的目的.针对对UWB信号的IMF重构过程中阶数阈值难以确定的问题,通过数值仿真的方法,得到信号分量和噪声分量在不同阶IMF上的能量分布特性;在对所得特性进行分析的基础上,设计了一种数据自适应的阶数阈值选取算法,解决了EMD消噪中的阶数阈值选取问题.仿真结果表明,EMD消噪算法能够在较低信噪比下提供平均10 dB的信噪比增益,可以有效地对超宽带信号进行消噪.     

基于EMD的齿轮故障识别研究

EMD(Emirical Moe Decompoition)方法是一种自适应的信号分解方法,该文根据齿轮故障振动信号的特点,将EMD方法应用于齿轮故障诊断中。研究结果表明,EMD方法可以有效地提取齿轮故障振动信号的特征。     

Internal wave parameter inversion based on Empirical Mode Decomposition

Wind, the result of earth rotation and other processes, inaugurate the phenomenon of oceanic internal waves (OIWs), including driving turbulence, affecting nutrient and biomass distribution, and resuspending sediment. Therefore, a good understanding to OIWs' characters becomes a vital component to enhance its monitoring and utilization. The parameter inversion was conducted in the article for the OIW based on the empirical mode decomposition (EMD) method. The experimental data, the advanced synthetic aperture radar (ASAR) image, was captured in Dongsha Islands surrounding area on July 22, 2011. Considering the formation mechanism of internal waves, two important issues in the EMD method—the curve fitting and end effects—were studied. After comparing different algorithms, the cubic spline interpolation (CSI) was used for curve fitting and the boundary full-wave (BFW) method was applied to inhibit the end effects. Used this inversion method, the internal wave signal was extracted from the ASAR image, the distance of the internal wave between peak and trough was calculated, and the half-width of soliton was obtained as well. In addition, the inversion result is consistent with the previous experimental findings, which indicates the effectiveness of our algorithm.     

基于有理四次Hermite插值和PSO的EMD包络线拟合算法

针对经典三次样条插值在EMD分解中存在undershoot现象,模态混叠问题及分段三次Hermite插值不够灵活等问题,提出一种基于有理四次Hermite插值和PSO的EMD包络线算法.该算法利用有理四次Hermite中的形状参数调整曲线形状,并采用粒子群优化算法从曲线簇中找到最优平滑包络线.通过仿真信号实验和非平稳信号实验,表明该方法能够有效克服传统方法带来的undershoot问题,改善模态混叠效应,同时分解后的IMF分量正交性和能量保存度指标亦均优于经典CSI方法和PCHI方法.     

振动信号趋势项提取方法研究

针对某车载武器振动位移测试中存在的严重趋势项干扰问题,提出了基于经验模态分解（Empirical Mode Decomposition,EMD）的信号处理方法.为有效提取趋势项,提出了一种新的趋势项判定方法.该方法根据振动信号相对时间轴对称的特点,通过比较各IMF分量与原始信号的均值判定该阶IMF分量是否为趋势项.模拟振动信号仿真证明了方法的有效性.最后对实测信号进行了EMD处理并最终重构了振动位移信号,与小波变换方法及一种定性的EMD趋势项判定方法进行了比较,结果表明提出的基于EMD的信号趋势项提取和判定方法有更大的优越性,有助于客观评价该武器性能.     

Study on denoising of continuous spectrum on-line monitoring signal of water quality with micro-reagents based on HHT

A continuous spectrum water quality on-line monitoring signal processing method based on Hilbert-Huang transform (HHT) is proposed in this paper, which combines the micro-reagent water quality on-line monitoring technology of sequential injection. The modulation signal and spectrum curve of each intrinsic mode function (IMF) component of the original spectrum signal were obtained by empirical mode decomposition (EMD). The water sample data of different concentrations in the continuous spectrum on-line monitoring system was analyzed by the HHT model. The noise signal was excavated to realize the noise reduction processing, and the reconstruction of the continuous spectrum signal was realized after the noise reduction processing was completed. The research results show that this method can effectively reduce the noise of continuous spectrum signals according to different signal-to-noise characteristics of continuous spectrum, and has convenient use, fast processing speed, and high resolution in the time-frequency domain, which effectively improves the stability and accuracy of the micro-reagent continuous spectrum water quality on-line monitoring system.     

基于EMD和小波去噪处理的信号瞬时参数提取

本文提出用小波去噪后再运用经验模式分解(EMD)和希尔伯特变换方法提取信号瞬时特征。该方法克服了直接运用EMD分解方法由大量噪声带来的不必要的干扰,减少了EMD存在的边界效应和分解层数,提高参数提取的准确性和时效性,使算法在信号瞬时特征提取中更具有应用和研究前景。     

基于EMD包络谱的飞行器健康诊断

针对飞行器结构系统声发射信号的非线性与非平稳特征,为实现飞行器结构部件的有效健康监测,提出了基于经验模态分解包络谱的飞行器健康诊断方法.该法首先对由声发射传感器募集到的飞行器关键部件原始声发射信号进行经验模态分解(EMD),提取其固有频率段的固有模态函数(IMF)信息,然后运用Hilbert变换对其进行处理得到各IMF的包络信号,由此可得其包络谱.通过包络谱的特征信息便可实现对飞行器结构部件的健康诊断.将该方法应用于某飞机真实水平尾翼疲劳试验所募集的声发射信号,结果表明,该法可监测出飞行器水平尾翼的健康状态,适用于飞行器结构部件的健康监测.     

An Analytical Expression for Empirical Mode Decomposition Based on B-Spline Interpolation

Although empirical mode decomposition (EMD) lacks a rigorous theoretical basis, it has attracted much attention for analyzing nonstationary signals adaptively. In this paper, the EMD method is investigated from a digital signal processing perspective. Based on an analysis of extrema sampling and B-spline interpolation, we show that the upper and lower envelopes of signals are formed by a succession of three basic operations: decimation of local extrema, interpolation, and filtering by a B-spline filter. We then show that some aliasing noise can be suppressed by the mean of the envelopes, though the extrema sampling is a sub-Nyquist sampling. For uniformly spaced extrema of signals, we derive a general analytical expression of intrinsic mode functions (IMFs) extracted by the EMD method from signals.     

Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation

Since mode mixing of empirical mode decomposition (EMD) is mainly caused by the intermittence and noise, we propose a novel method to eliminate mode mixing of EMD based on the revised blind source separation. To this aim, an optimal morphological filter is employed to eliminate the noise. As a result, the component of mode mixing caused by noise is suppressed. Furthermore, the de-noised signal is decomposed into different intrinsic mode function (IMF) components through the EMD algorithm. Since it is impossible to apply blind source separation to a single channel signal directly, the IMF component, which has mode mixing is chosen and reconstructed in the phase space. Following that, the equivalent hypothetical signals are obtained. Finally, an improved fixed-point algorithm based on independent component analysis (ICA) is introduced to separate the overlapping components. The analysis of simulation and practical application demonstrates that our proposed method can effectively tackle the mode mixing problem of EMD.     

一种改进的基于经验模态分解的小波阈值滤波方法

经验模态分解是一种新的信号分解方法,该方法可将非线性非平稳信号分解成若干个单分量的本征模态函数,使得每个本征模态函数都具有一定的物理意义。本文探索了该方法在语音增强方面的应用.在文献[8]的基础上,对其方法进行了有效改进。首先将带噪语音进行经验模态分解,得到六个本征模态函数和一个余量信号,对这七个信号分别进行小波阈值滤波,并由滤波后的七个信号重构语音。结果表明,该方法的滤波效果明显优于对带噪语音直接采用小波阈值滤波的方法,并且较之文献[8]的滤波方法也具有一定的优势。     

基于经验模态分解的模态域MVDR方法研究

 矢量阵MVDR波束形成可有效地实现信号的空间谱估计,但它仅适用于窄带信号,当各目标强度相差较大时,难以实现对弱目标的有效检测.经验模态分解具有突出信号局瞬特征的特点,可将多分量信号分解成多阶固有模态函数.结合固有模态函数特性和MVDR窄带信号要求,提出了矢量阵模态域MVDR波束形成算法,并将中心频率的概念应用于固有模态函数,以此作为模态域MVDR波束形成算法的中心频率.海试结果表明:本方法可增强弱目标所在方位空间谱的能量,有效地实现强干扰下弱目标的检测.