时频分布重排方法在水下目标回声识别中的应用

特征提取是目标识别的关键技术,本文提出了一种基于时频分布重排的Wigner-Ville分布(WD)特征提取方法。选择WD作为研究问题的切入点,使用核函数方法,通过平滑伪Wigner-Ville分布(SPWD)抑制交叉项;为解决引入核函数导致信号时频聚集性下降问题,我们采用时频重排方法来增强目标的时频聚集性,最后用海上实验数据对本文方法进行了有效性检验。实验结果表明,本文提出的SPWD时频重排特征提取方法具有:(1)可有效抑制WD固有的交叉项;(2)增强目标回声信号时频聚集性;(3)可明显地提高水下目标识别率。该方法具有一定的应用推广价值,为解决水下目标识别问题提供了很好的思路。     

光纤光栅马赫-曾德干涉仪条纹信号的傅里叶分析

基于长周期光纤光栅马赫-曾德干涉仪的频分复用技术是实现光纤多参量传感的重要途径.研究了频分复用中复合条纹信号的傅里叶处理方法,分析了傅里叶频谱中和频与差频信号的幅值与单个干涉条纹信号的关系,提出了抑制和频与差频信号的有效方法,即改变光栅结构参量适当降低中心波长处的条纹对比度以提高频谱信号的信噪比,增加特征频谱滤波的有效性.结果表明:经过优化调整的傅里叶频谱信噪比可以提高至原来的2倍以上;对比频谱调整后恢复相位的余弦曲线与原条纹信号发现,频谱信噪比提高的同时保留了反映各单个原条纹特征的足够相位信息.该光谱优化方法可为基于光纤光栅马赫-曾德干涉仪和频分复用的光纤多参量传感技术提供理论与技术指导.     

改进的基于Spatial WVD和Hough变换的近场信源参量估计及性能分析

利用近场信源的一次快拍数据具有近似线性调频信号形式这一特点,将空域—频域联合平滑WVD和边缘检测以及直线拟合技术结合,对边缘检测结果进行Hough变换得到候选点集,并采用最小二乘聚类拟合获得抑制交叉项与噪音影响的初始频率和调频率估计,进而估计出近场信源的波达角和距离参量.理论分析和仿真结果表明,该方法能够显著抑制WVD分布的交叉项,正确拟合信源的原始空域—频域分布,并具有较低的运算复杂度.     

激光多普勒振动信号的时频分析

激光多普勒雷达实际测得的振动信号绝大多数都是时变信号,而基于傅立叶变换的时频分析方法是处理时变信号的有利工具。本文针对激光多普勒雷达测得的实际振动信号,比较了魏格纳-维利分布、平滑伪魏格纳-维利分布、频谱图、波恩-约旦分布和扩展修正B分布5种形式的时频分析性能。利用激光多普勒雷达测量实际单音响产生的啁啾信号振动、双音响产生的二分量啁啾信号振动、以及成年男性心跳振动3种振动,分析了时频图的分辨率和交叉项抑制情况,并通过计算时频聚集度指数,比较了5种分布情况下振动的分析性能。实验证明,扩展修正B分布的性能优于其他4种时频分布,扩展修正B分布更适合应用于激光多普勒雷达材料共振频率探测和心跳检测领域。     

时频-Radon变换在激光微多普勒效应特征提取中的应用

鉴于激光微多普勒效应探测运动目标复合振动的重要性,利用离散小波多分辨分析对激光微多普勒效应探测运动目标复合振动的实验数据进行分解,研究不同尺度下的信号特征。将Radon变换与时频分析结合在一起,对实验所得信号在时频域中进行分析,结果表明:时频-Radon变换适合于微多普勒信号的识别,应用该方法能够抑制多分量信号时频分布产生的交叉项干扰,有效地提取微动目标的特征,为目标特征的识别、分类和探测提供了便利。     

基于改进自适应分解法的单通道雷达引信混合信号分离

为了更好地改善单通道多分量调频体制雷达引信信号的时频分布,以便于完成参数估计和信号分离,将自适应chirplet分解法引入到单通道雷达引信信号的分离问题当中,并加入时频曲线弯曲度因子,提出一种改进的自适应chirplet分解法,使其能够被用于非线性相位信号的分解,利用快速算法和优化算法,优化了运算过程,可准确且快速地求解出各参数,得到无交叉项的多分量信号时频分布,使复杂混合情况下的多分量信号也能够被较好地分离.通过仿真,利用该方法对多种情况下混合信号的时频分布进行了很好的改进,而后分解出了各分量信号,并通过分析计算出了相应的混合系数,仿真结果说明了该方法的有效性,且具有一定的抗噪声性能.     

基于光纤光参量放大的异步双波长全光再生技术研究

本文提出了一种新型的多波长全光再生方案,利用相位时钟光纤光参量放大,并采用相邻信道偏振正交的方法,实现对由异步信源产生的双波长信号全光再生.理论分析了参量放大中的增益饱和现象用于幅度噪声抑制,以及利用相位时钟及后续色散实现对信号定时的机理.在这个基础上,对两个独立信源产生的异步双波长10Gbit/s信号进行再生实验,实验表明该方案有效的抑制了基于多波长3R再生系统中信道间的四波混频与交叉相位调制等非线性干扰.系统在单波长和双波长情况下分别将两路信号信噪比改善了至少6.5dB与4.5dB.误码率测试结果说明,与背对背测试结果相比,无论是在单波长还是双波长条件下,两路波长的信号经过再生后都实现了约2dB的接收机功率代价的改善.     

基于Nuttall窗三谱线插值的激光多普勒信号处理方法研究

快速傅里叶变换(FFT)是主要的激光多普勒测速系统信号处理方法,由于受到频谱泄露和栅栏效应的影响,其处理精度并不理想。基于Nuttall窗三谱线插值法的激光多普勒信号处理方法就是一种好的处理方法,它可以有效地抑制频谱泄漏和栅栏效应,改善信号分析精度,提高速度测量精度。由于Nuttall四项余弦窗的能量集中在主瓣,旁瓣非常小,因此加Nuttall四项余弦窗的FFT三谱线插值算法能够极大地减小频谱泄漏的影响,谱间干扰也很小,能很好地减小频谱泄漏和栅栏效应。     

基于伪魏格纳分布分解的合成孔径雷达图像目标与阴影分割

在二维魏格纳分布的框架内,针对魏格纳变换的交叉项问题和计算量大的问题,提出了合成孔径雷达图像局部伪魏格纳变换的目标和目标阴影的分割方法.首先,将合成孔径雷达图像进行二维伪魏格纳变换,得到各像素点的二维能量谱图;然后提取各像素点的二维能量谱图对应位置值形成多个不同频段的与原图像同大小的能量谱图;最后,对不同频段的能量谱图采用不同的处理方法后,将各能量谱图相加处理后形成区域标识图像,最终得到原图像的目标和目标阴影分割图像.本文利用该方法对MSTAR切片图像进行了分割试验,并对分割图像与频谱最大值距离或方位分割算法和基于双参量CFAR与隐马尔科夫联合分割算法进行了分割图像对比度对比.实验结果表明,采用本文算法的合成孔径雷达分割图像,对比度明显提高,且保留了目标图像细节.     

基于伪魏格纳分布分解的合成孔径雷达图像目标与阴影分割

在二维魏格纳分布的框架内,针对魏格纳变换的交叉项问题和计算量大的问题,提出了合成孔径雷达图像局部伪魏格纳变换的目标和目标阴影的分割方法.首先,将合成孔径雷达图像进行二维伪魏格纳变换,得到各像素点的二维能量谱图|然后提取各像素点的二维能量谱图对应位置值形成多个不同频段的与原图像同大小的能量谱图|最后,对不同频段的能量谱图采用不同的处理方法后,将各能量谱图相加处理后形成区域标识图像,最终得到原图像的目标和目标阴影分割图像.本文利用该方法对MSTAR切片图像进行了分割试验,并对分割图像与频谱最大值距离或方位分割算法和基于双参量CFAR与隐马尔科夫联合分割算法进行了分割图像对比度对比.实验结果表明,采用本文算法的合成孔径雷达分割图像,对比度明显提高,且保留了目标图像细节.     

Time-frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation

Energy separation algorithm is good at tracking instantaneous changes in frequency and amplitude of modulated signals, but it is subject to the constraints of mono-component and narrow band. In most cases, time-varying modulated vibration signals of machinery consist of multiple components, and have so complicated instantaneous frequency trajectories on time-frequency plane that they overlap in frequency domain. For such signals, conventional filters fail to obtain mono-components of narrow band, and their rectangular decomposition of time-frequency plane may split instantaneous frequency trajectories thus resulting in information loss. Regarding the advantage of generalized demodulation method in decomposing multi-component signals into mono-components, an iterative generalized demodulation method is used as a preprocessing tool to separate signals into mono-components, so as to satisfy the requirements by energy separation algorithm. By this improvement, energy separation algorithm can be generalized to a broad range of signals, as long as the instantaneous frequency trajectories of signal components do not intersect on time-frequency plane. Due to the good adaptability of energy separation algorithm to instantaneous changes in signals and the mono-component decomposition nature of generalized demodulation, the derived time-frequency energy distribution has fine resolution and is free from cross term interferences. The good performance of the proposed time-frequency analysis is illustrated by analyses of a simulated signal and the on-site recorded nonstationary vibration signal of a hydroturbine rotor during a shut-down transient process, showing that it has potential to analyze time-varying modulated signals of multi-components.     

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.     

基于EMD的谱峭度方法在滚动轴承故障检测中的应用

传统谱峭度方法通常采用基于短时傅里叶变换(Short Time Fourier Transform,STFT)的峭度图方法来实现。针对STFT不能保证对瞬态脉冲这种高度非平稳信号最优的分解效果的缺点,提出一种基于经验模式分解(Empirical Mode Decomposition ,EMD)的谱峭度方法。该方法首先利用EMD和Hilbert变换得到信号的时频分布,然后将信号的时频分布按照不同层数分成若干频段,通过计算各频段的峭度值得到相应的峭度图,再根据峭度最大原则选择滤波频段进行带通滤波,最后对滤波信号采用包络分析确定故障信息。实验结果表明：相比传统基于STFT的谱峭度方法,本文方法更能准确的获得轴承加速度信号的故障特征频率信息。     

Blind speech source separation via nonlinear time-frequency masking

Aim at the underdetermined convolutive mixture model, a blind speech source separation method based on nonlinear time-frequency masking was proposed, where the approximate W-disjoint orthogonality （W-DO） property among independent speech signals in time-frequency domain is utilized. In this method, the observation mixture signal from multimicrophones is normalized to be independent of frequency in the time-frequency domain at first, then the dynamic clustering algorithm is adopted to obtain the active source information in each time-frequency slot, a nonlinear function via deflection angle from the cluster center is selected for time-frequency masking, finally the blind separation of mixture speech signals can be achieved by inverse STFT （short-time Fourier transformation）. This method can not only solve the problem of frequency permutation which may be met in most classic frequency-domain blind separation techniques, but also suppress the spatial direction diffusion of the separation matrix. The simulation results demonstrate that the proposed separation method is better than the typical BLUES method, the signal-noise-ratio gain （SNRG） increases 1.58 dB averagely.     

基于非线性时频掩蔽的语音盲分离方法

针对语音信号的欠定卷积混合模型,利用独立语音在时频域上的近似W-分离正交性(W-DO),提出了一种基于非线性时频掩蔽的盲分离方法。首先对多传声器观测信号在时频域上进行规范化处理,使混合信号在每个时频槽的表示与频率无关,然后采用动态聚类算法获取时频槽对应的活跃源信息,选择关于簇中心偏角的非线性函数进行时频掩蔽,从而实现语音信号的盲分离。该方法解决了经典频域盲分离算法中的频率置换问题,能有效抑制分离矩阵的空间方向扩散。仿真实验表明,与BLUES方法相比具有更优的分离性能,信噪比增益平均增加1.58 dB。     

Noise reduction combining time-domain epsilon-filter and time-frequency epsilon-filter

A time-domain epsilon-filter (TD epsilon-filter) is a nonlinear filter that can reduce noise while preserving a signal that varies drastically, such as a speech signal. Although the filter design is simple, it can effectively reduce noise. It is applicable not only to stationary noise but also to nonstationary noise. It cannot, however, be applied when the amplitude of noise is relatively large. This paper introduces an advanced method for noise reduction that applies an epsilon-filter to complex spectra, namely a time-frequency epsilon-filter (TF epsilon-filter). This paper also introduces noise reduction combining a TD epsilon-filter and a TF epsilon-filter. An advanced method called a variable time-frequency epsilon-filter is also proposed. First, the algorithm of the TD epsilon-filter is explained to clarify the problem. Then, the algorithms of the proposed methods are explained. By utilizing an epsilon-filter in the frequency domain, the proposed method can reduce not only noise that has a relatively small amplitude but also noise that has a relatively large amplitude. Experimental results are also given to demonstrate the performance of the proposed methods in comparison to the results of some conventional methods.     

The concise fractional Fourier transform and its application in detection and parameter estimation of the linear frequency-modulated signal

A concise fractional Fourier transform(CFRFT) is proposed to detect the linear frequency-modulated(LFM) signal with low signal to noise ratio(SNR).The frequency axis in time-frequency plane of the CFRFT is rotated to get the spectrum of the signal in different angles using chirp multiplication and Fourier transform(FT).For LFM signal which distributes as a straight line in time-frequency plane,the CFRFT can gather the energy in the corresponding angle as a peak and improve the detection SNR,thus the LFM signal of low SNR can be detected.Meanwhile,the location of the peak value relates to the parameters of the LFM signal.Numerical simulations and experimental results show that,the proposed method can be used to efficiently detect the LFM signal masked by noise and to estimate the signal's parameters accurately.Compared with the conventional fractional Fourier transform(FRFT),the CFRFT reduces the transform complexity and improves the real-time detection performance of LFM signal.     

多尺度分析在激光微多普勒效应特征提取中的应用

由于小波变换具有准确度高、抗噪性好等优点,在微多普勒效应探测方面具有很好的应用前景。利用多尺度分析（MRA）将信号进行分解并提取出含有微多普勒效应的低频平滑信息,获取了目标微小振动引起的微多普勒效应。用频谱分析和基于瞬时频率(Instfreq)的时-频域联合分析方法对原始信号及通过MRA分解得到的结果进行了对比研究。结果表明：利用时-频分析对MRA处理后的低频信号进行分析,可更加有效地提取微多普勒效应中振动信号的特征,为目标特征的识别、分类和探测提供了便利。