混沌干扰中基于同步挤压小波变换的谐波信号提取方法

针对混沌干扰背景下多个谐波信号的提取问题, 提出了一种基于同步挤压小波变换(SST)的谐波信号抽取方法. 首先利用SST将混沌信号和谐波信号组成的混合信号分解为不同的内蕴模态类函数, 然后利用Hilbert变换对分离出的内蕴模态类函数进行频率识别, 从中分离出各谐波信号. 以Duffing混沌背景为例, 对混沌干扰下多谐波信号的提取进行了实验分析. 实验结果表明: 对于不同频率间隔的多个谐波分量, 本文方法的提取结果都具有较高的精度, 而且所提方法对高斯白噪声的干扰具有较好的鲁棒性, 综合提取效果优于经典的经验模态分解方法.     

混沌海杂波背景下的微弱信号检测混合算法

基于经验模态分解理论, 提出了一种基于粒子群算法的支持向量机预测方法. 采用总体平均经验模式分解法将混沌信号分解为若干固有模态函数和趋势分量, 将复杂的非线性信号转化为具有不同尺度特征的平稳分量. 利用粒子群算法对支持向量机的惩罚系数和核函数进行优化, 结合支持向量机建立混沌序列的单步预测模型. 从预测误差中检测淹没在混沌背景中的微弱信号(包括瞬态信号和周期信号). 对Lorenz系统和实测IPIX雷达数据进行仿真实验, 结果表明, 该方法能够有效地从混沌背景噪声中检测出微弱目标信号, Lorenz系统得到的均方根误差0.000000339 (-102.8225 dB时)比传统支持向量机方法的均方根误差0.049 (-54.60 dB时)降低了5个数量级, 从海杂波中检测出具有谐波特性的微弱信号, 表明预测模型具有更低的门限和误差.     

基于独立成分分析和经验模态分解的混沌信号降噪

基于经验模态分解和独立成分分析去噪的特点,提出了一种联合独立成分分析和经验模态分解的混沌信号降噪方法. 利用经验模态分解对混沌信号进行分解,根据平移不变经验模态分解的思想构造多维输入向量, 通过所构造的多维输入向量和独立成分分析对混沌信号的各层内蕴模态函数进行自适应去噪处理; 将处理后的所有内蕴模态函数进行累加重构,从而得到降噪后的混沌信号. 仿真实验中分别对叠加不同强度高斯噪声的Lorenz混沌信号及实际观测的月太阳黑子混沌序列进行了研究, 结果表明本文方法能够对混沌信号进行有效的降噪,而且能够较好地校正相空间中点的位置, 逼近真实的混沌吸引子轨迹. 关键词： 独立成分分析 经验模态分解 混沌信号 降噪     

混沌背景下信号的盲分离

混沌信号与确定性小信号叠加生成的混合信号是一更高维的混沌信号,因而不能用一般的混沌信号噪声抑制的方法进行分离.提出了一种这类信号盲分离的方法.在重构未知的混沌信号的动力方程时,充分利用混沌吸引子的几何特性,并且限定动力映射为原混沌吸引子所在流形的内部映射,从而保证了重构的动力系统方程对应于原混沌信号,而不是同样具有混沌特性的混合信号.然后利用重构的动力方程,借用混沌信号中的噪声抑制思想,估计出原混沌信号对应的轨道,实现信号分离.通过对Lorenz系统中谐波信号、Henon映象中自回归过程,以及脑电信号中谐波信号进行提取的数值实验,验证了信号盲分离方法的有效性和可行性. 关键词： 混沌 非线性 信号处理 盲分离     

基于EMD方法的混沌时间序列预测

将经验模态分解(EMD)方法引入到非线性数据处理中,提出用EMD分解后的数据进行混沌预测的方法.通过Duffing方程和Lorenz系统的非线性响应预测实例表明,EMD分解后的信号和原始信号相比具有较小的最大Lyapunov指数,可提高预测时间和长时预测精度. 关键词： EMD 混沌 预测     

激光雷达信号的可变间隔阈值经验模式分解去噪法

针对采用经验模式分解直接阈值(EMD-DT)和经验模式分解间隔阈值(EMD-IT)在激光雷达回波信号的去噪应用中会产生的模态混叠现象,采用一种可变间隔阈值的经验模式分解（EMD-SIT）的去噪方法。首先,对信号进行经验模式分解。然后,采用过零率方法将分解出的含有噪声的固有模态函数分离。最后,应用过零点阈值,设立一个新的可变阈值,将EMD-IT和EMD-DT有效融合对信号进行去噪。通过与多种阈值的仿真对比以及激光雷达的回波信号去噪实验,结果表明该方法可以有效地去除噪声,抑制模态混叠,较EMD-IT和EMD-DT更具有优越性,因此有着很好的应用前景。     

利用小波多尺度分解算法实现混沌系统的噪声减缩

应用小波多尺度分解算法进行噪声减缩,从混沌背景中分离周期信号、噪声及其他混沌信号.小波多尺度分解算法能够区分不同尺度的信号是利用小波变换在时、频两域具有突出信号特征的能力以及小波变换是一线性变换的特点.提出的方法仅利用信号的尺度特性,克服了先前的噪声减缩要知道产生混沌信号的数学模型,并且要求叠加在混沌背景中的其他信号的幅度相对混沌背景信号的幅度很小的假定.给出了从Lorenz混沌背景中提取正弦信号、白噪声和Chua's电路产生的混沌信号的计算机模拟结果. 关键词：     

基于经验模态分解的物体入水声检测及测向研究

物体入水声是一种瞬态信号,其波形由击水声和若干气泡脉动组成。传统的矢量信号处理方法对此类瞬态信号的检测和测向会出现困难,尤其是在信噪比较低时检测不到入水声信号。经验模态分解是一种突出信号局部瞬态特性的非线性分析方法,将矢量传感器接收的声压、振速信息分解为不同的固有模态函数,利用文中提出的模态声强器的方位估计算法,可以实现瞬态信号的检测和测向。湖试和海试结果表明该方法能把本地干扰和入水声分解到不同的模态函数中,利用模态声强器可以在本地强干扰下有效检测到入水声信号出现的时间,并可以实现测向。     

基于经验模态分解消噪的光纤光栅解调系统

为了提高光纤Bragg光栅解调系统的解调准确度,提出利用经验模态分解对信号进行滤波分析和降噪处理的方法.该方法将经验模态分解得到的固有模态函数,分为信号分量起主导作用模态与噪音分量起主导作用模态,并利用反映信号主要结构的模态对信号进行重构实现去噪.实验表明,解调系统输出信号能够识别出中心波长的位置,精确得到Bragg波长的漂移量,输出谱失真小、信噪比高.对温度实验数据进行曲线拟合,拟合线性度为0.998,提高了系统的解调准确度.     

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     

Harmonic signal extraction from noisy chaotic interference based on synchrosqueezed wavelet transform

For the harmonic signal extraction from chaotic interference, a harmonic signal extraction method is proposed based on synchrosqueezed wavelet transform(SWT). First, the mixed signal of chaotic signal, harmonic signal, and noise is decomposed into a series of intrinsic mode-type functions by synchrosqueezed wavelet transform(SWT) then the instantaneous frequency of intrinsic mode-type functions is analyzed by using of Hilbert transform, and the harmonic extraction is realized. In experiments of harmonic signal extraction, the Duffing and Lorenz chaotic signals are selected as interference signal, and the mixed signal of chaotic signal and harmonic signal is added by Gauss white noises of different intensities.The experimental results show that when the white noise intensity is in a certain range, the extracting harmonic signals measured by the proposed SWT method have higher precision, the harmonic signal extraction effect is obviously superior to the classical empirical mode decomposition method.     

强混沌干扰中的谐波信号提取

提出一种新的从强混沌干扰中提取被掩蔽的谐波信号的方法.依据混沌吸引子固有的几何性质,借助微分流形切空间的概念,将混沌干扰和谐波信号分别投影到混沌吸引子所在流形的切空间及其补空间,从而成功地将二者分离.理论分析和计算机仿真实验都表明该方法非常有效 关键词： 混沌 非线性 信号处理     

浅海波导中简正波干涉特征频率增强

针对浅海波导中水平线列阵接收的低频宽带低信噪比信号,提出了一种利用多拍信号相干累加来提高干涉简正波特征频谱信噪比的方法。这种方法主要针对未知距离和声源形式的运动声源,对不同时刻(或接收距离)处阵列输出信号进行warping变换,根据干涉简正波的特征频率不变性,通过搜索距离与径向速度的比值来对齐未知距离下warping变换后的谱峰频率,进而通过相干累加来增强信号干涉简正波的特征频率。仿真和海上实验数据分析均验证了方法的性能。     

具有格点各向异性的一维铁磁链中的量子孤子和内禀局域模(英文)

基于Hartree-Fock方法和多标度方法,我们考察了具有格点各向异性的一维铁磁链中的量子孤子和内禀局域模,量子磁振子的波函数由量子包络孤子来描述.在布里渊区边界,量子包络孤子变成了量子内禀局域模,它的量子本征频率在简谐波带的顶部上方,量子磁振子主要集中在中心位置j=j0的附近.     

Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis

Accurately identifying faults in rotor-bearing systems by analyzing vibration signals, which are nonlinear and nonstationary, is challenging. To address this issue, a new approach based on ensemble empirical mode decomposition (EEMD) and self-zero space projection analysis is proposed in this paper. This method seeks to identify faults appearing in a rotor-bearing system using simple algebraic calculations and projection analyses. First, EEMD is applied to decompose the collected vibration signals into a set of intrinsic mode functions (IMFs) for features. Second, these extracted features under various mechanical health conditions are used to design a self-zero space matrix according to space projection analysis. Finally, the so-called projection indicators are calculated to identify the rotor-bearing system?s faults with simple decision logic. Experiments are implemented to test the reliability and effectiveness of the proposed approach. The results show that this approach can accurately identify faults in rotor-bearing systems.     

A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: intrinsic time-scale decomposition based on time-varying filtering. It is introduced into the framework of complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN). Compared with traditional intrinsic time-scale decomposition, intrinsic time-scale decomposition based on time-varying filtering can improve frequency-separation performance. It has strong robustness in the presence of noise interference. However, decomposition parameters (the bandwidth threshold and B-spline order) have significant impacts on the decomposition results of this method, and they need to be artificially set. Aiming to address this problem, this paper proposes rolling-bearing fault diagnosis optimization based on an improved coyote optimization algorithm (COA). First, the minimal generalized refined composite multiscale sample entropy parameter was used as the objective function. Through the improved COA algorithm, optimal intrinsic time-scale decomposition parameters based on time-varying filtering that match the input signal are obtained. By analyzing generalized refined composite multiscale sample entropy (GRCMSE), whether the mode component is dominated by the fault signal is determined. The signal is reconstructed and decomposed again. Finally, the mode component with the highest energy in the central frequency band is selected for envelope spectrum variation for fault diagnosis. Lastly, simulated and experimental signals were used to verify the effectiveness of the proposed method.     

Experimental investigation of the pulse inversion technique for imaging ultrasound contrast agents

The application of ultrasound contrast agents aims to detect low velocity blood flow in the microcirculation. To enhance discrimination between tissue and blood containing the contrast agent, harmonic imaging is used. Harmonic imaging requires the application of narrow-band signals and is obscured by high levels of native harmonics generated in an intervening medium. To improve discrimination between contrast agent and native harmonics, a pulse inversion technique has been proposed. Pulse inversion allows wide-band signals, thus preserving the axial resolution. The present study examines the interference of native harmonics and discusses the practical difficulties of wide-band pulse inversion measurements of harmonics by a single transducer. Native harmonics are not eliminated by pulse inversion. Furthermore, only even harmonics remain and are amplified by 6 dB, alleviating the requirement for selective filtering. Finally, it is shown that the contaminating third harmonic contained in the square wave activation signal leaks through in the emitted signal. The spectral location of the contaminating third harmonic is governed by the transducer spectral characteristics while the location of the native and contrast agent second harmonics is not. Thus the contaminating third harmonic and the native and contrast agent second harmonics may overlap and interfere. Optimal discrimination requires a balance between maximal sensitivity for the second harmonic at reception and minimal interference from the contaminating third harmonic.     

基于希尔伯特黄变换的自适应相位提取法

提出了一种基于希尔伯特黄变换的自适应相位提取法。该方法通过对条纹图信号进行经验模态分解得到一系列本征模函数(IMF)。对每个IMF进行希尔伯特谱分析,提出准则用以确定噪声IMF并判断是否存在模式混叠问题。若存在,根据该噪声IMF自适应设计新的“噪声”并将其添加到原信号中,然后对所形成的新信号再次分解,重复进行该过程直到相应的模式混叠问题不再存在。将最后一次分解所得的噪声IMF和背景分量从信号中去除,对所得的基频分量做希尔伯特变换即可得到条纹图的包裹相位分布。所提方法可有效克服模式混叠问题,可在有效去除噪声和背景分量的同时尽量保留细节相位信息,有较好的自适应性及稳健性,测量精度高。     

Sensitive signal detection using a feed-forward oscillator network

We present the results of an experimental investigation of a network of nonlinear coupled oscillators which are coupled in feed-forward mode. By exploiting the nonlinear response of each oscillator near its intrinsic Hopf bifurcation point, we have found remarkable amplification of small signals over a narrow bandwidth with a large dynamic range. The effect is exploited to extract a small amplitude periodic signal from an input time series which is dominated by noise. Specifically, we have used this relatively simple experimental system to measure responses with a bandwidth of approximately 1% of the central frequency, amplifications of approximately 60 dB, and a dynamic range of approximately 80 dB and can extract signals from a time series with a signal to noise ratio of approximately -50 dB.