用于光纤围栏入侵告警的频谱分析快速模式识别

相位敏感光时域反射计(Ф-OTDR)在光纤围栏等动态传感领域具有重要的应用,快速、有效地对入侵信号分类识别有着十分重要的意义。基于频谱分析提出了一种称为频谱欧氏距离法(EDFS)的快速模式识别方法。该方法通过短时平移差分和短时能量法对Ф-OTDR的解调信号进行提取,确定待分析数据段;对数据段进行归一化和快速傅里叶变换,获得信号的频谱特征;计算信号频谱与预先生成的模板之间的欧氏距离对入侵信号进行分类、识别。采用三种入侵信号对该方法的有效性和实时性进行了实验验证。结果表明,该模式识别方法可以有效识别扰动信号,识别时间小于传统的动态时域规划模式识别方法耗时的1/10。同时,该方法所需训练样本较少,对环境噪声有一定程度的抑制作用。     

非平稳信号分析的广义解析模态分解方法

现有的非平稳信号分析方法都有各自不同的缺陷,短时傅里叶变换的时频分辨率受不确定性原理的限制,希尔伯特黄变换存在端点效应和模态混叠,易导致模糊的时频分布;解析模态分解只适合分析频率恒定的多分量信号;针对包含多个时变模态、特别是频谱重叠的非平稳信号,本文提出了一种新的信号分析方法———广义解析模态分解（Generalized Analytical Mode Decomposition,GAMD）．GAMD通过广义傅里叶变换将时变频率转换为频谱可分的,采用解析模态分解对其分解,再对得到的单分量信号进行逆广义傅里叶变换即可得到原始信号的分量．因此,GAMD非常适合分析时变的非平稳信号．通过仿真信号将GAMD与短时傅里叶变换和希尔伯特黄变换等方法进行了对比,结果表明GAMD方法的分解效果更精确,时频分辨率更高．     

基于时频谱融合的加速度计信号分析

时频谱分析可以提供信号在时间域和频率域的联合分布信息,针对该方法很难同时保证较高的时间分辨率和频率分辨率的问题,提出了一种时频聚集性很高的谱融合方法。先用谐波小波包将信号分解到不同频段,再用高时间分辨率的Morlet小波和高频率分辨率的短时傅里叶变换分别对各个频段上的分量进行分析,得到小波尺度矩阵和短时傅里叶变换时频矩阵,然后通过算法将二者融合在一个时频谱中。通过仿真和对动态条件下加速度计信号的分析,证明该算法既能提取出微弱的动态变化,又具有较高的时频分辨率,可以直观、全面、精确地对信号进行识别。     

基于时频加窗短时傅里叶变换的LFM干扰抑制

通过对线性调频(LFM)信号分数阶傅里叶变换的分析,该文提出了一种基于时频加窗短时傅里叶变换(TFW-STFT)的LFM干扰抑制算法。由于提出的时频窗对LFM干扰具有较好的频域能量聚集性能,因此TFW-STFT对信号的影响要小于无聚集性能的短时傅里叶变换。仿真结果证明该算法在信噪比损失和系统误比特率上明显优于基于短时傅里叶变换的算法。     

基于时频分析的扬声器异音故障诊断方法

提出了基于声信号时频分析的扬声器异音故障诊断方法,利用短时傅里叶变换和小波包变换2种时频分析方法将扬声器一维响应信号转化为二维时频图像,并利用3种距离方法对不同类型的扬声器时频图像进行了分析。结果表明,2种时频分析方法可充分反映故障的不同类型,3种距离方法可快速识别扬声器是否合格。     

基于线性正则变换与短时傅里叶变换联合的时频分析方法

线性正则变换作为傅里叶变换、分数阶傅里叶变换更为广义的形式,已经在光学和信号处理等领域得到了应用.短时傅里叶变换是一种线性时频分布,避免了其他双线性时频分布中出现的交叉项干扰,是分析时频信号的有力工具.本文从线性正则变换的定义和性质出发,研究了线性正则变换与短时傅里叶变换的时频关系,提出了基于线性正则变换与短时傅里叶变换联合的时频分析方法,避免了交叉项问题能够实现chirp信号干扰抑制和多分量时频信号分离.最后用仿真实例表明,该方法是分析时频信号的有效手段.     

信号的时频分析理论与应用评述

由傅里叶变换在刻划信号的时间信息和频率信息上的矛盾引出了时频联合分析的思想,在详细介绍了短时傅里叶变换、小波变换、魏格纳威利分布和希尔伯特黄变换这4种典型时频分析方法的基础上,对他们的时频局部性能进行了分析和比较,指出了这些分析方法的优势和存在的问题。最后简要介绍了上面4种典型时频分析方法各自的应用领域。     

短时分数阶傅里叶变换对调频信号的时频分辨能力

调频信号的检测和参数估计一直是信号处理领域的研究热点之一。为深入挖掘短时分数阶傅里叶变换对调频信号的时频分析优势,从短时分数阶傅里叶变换的定义出发,推导了其时频分辨能力与信号参数的关系,并与短时傅里叶变换进行了对比分析。结论表明,短时傅里叶变换时频分辨能力与信号频率变化率有关,而短时分数阶傅里叶变换几乎不受调频率变化率影响。最后,通过对比仿真实验证明,对于频率变化率较小的信号,两者时频分辨效果差别不明显,对于频率变化率较大的信号,短时分数阶傅里叶变换的时频分辨效果更好。     

一种雷达辐射源智能个体识别的方法

针对使用传统的卷积神经网络及低信噪比环境下雷达辐射源智能个体识别研究中识别性能不够的问题,提出了一种基于短时傅里叶变换(STFT)和EfficientNet的雷达辐射源个体识别方法。首先对雷达信号进行短时傅里叶变换,提取时频特征,然后利用EfficientNet中多个MBconv模块对不同时频特征图像的叠加,挖掘出信号图像隐含的更加复杂和抽象的深层次时频特征,包括信号强度的分布、时频模式、周期性变化等,从而完成个体分类识别。EfficientNet可以同时改变网络深度、宽度、图像分辨率3个参数,解决了梯度消失、梯度爆炸等问题。实验结果表明,基于STFT和EfficientNet的雷达辐射源智能个体识别的方法,相比于传统卷积神经网络在低信噪比环境下具有更好的识别性能。     

基于同步压缩短时傅里叶变换的毫米波雷达人体动作识别

针对人体动作识别问题，提出一种基于同步压缩短时傅里叶变换的人体动作识别方法。使用毫米波雷达进行人体动作数据的采集，将采集到的数据进行同步压缩短时傅里叶变换得到其时频图；然后使用卷积神经网络对不同动作进行微多普勒特征提取并分类。在数据采集部分，使用毫米波雷达进行数据采集，有效地避免了外界因素的影响；在时频分析部分，使用窗函数优化的同步压缩短时傅里叶变换提高了时频聚集性。实验结果表明，该人体动作识别系统对不同人体动作的识别率可达到91.7%。     

Network intrusion detection method based on matrix factorization of their time and frequency representations

In the last few years, detection has become a powerful methodology for network protection and security. This paper presents a new detection scheme for data recorded over a computer network. This approach is applicable to the broad scientific field of information security, including intrusion detection and prevention. The proposed method employs bidimensional (time-frequency) data representations of the forms of the short-time Fourier transform, as well as the Wigner distribution. Moreover, the method applies matrix factorization using singular value decomposition and principal component analysis of the two-dimensional data representation matrices to detect intrusions. The current scheme was evaluated using numerous tests on network activities, which were recorded and presented in the KDD-NSL and UNSW-NB15 datasets. The efficiency and robustness of the technique have been experimentally proved.     

A linear model for TF distribution of signals

We describe a new linear time-frequency model in which the instantaneous value of each signal component is mapped to the curve functionally representing its instantaneous frequency. This transform is linear, uniquely defined by the signal decomposition, and satisfies linear marginal-like distribution properties. We further demonstrate the transform generated surface may be estimated from the short time Fourier transform by a concentration process based on the phase of the short-time Fourier transform (STFT), differentiated with respect to time. Interference may be identified on the concentrated STFT surface, and the signal with the interference removed may be estimated by applying the linear-time-marginal to the concentrated STFT surface from which the interference components have been removed.     

基于同步压缩短时傅里叶变换的微型无人机识别

针对利用雷达微多普勒效应的微型无人机识别问题,提出了一种基于同步压缩短时傅里叶变换(Synchrosqueezing Short-Time Fourier Transform,SSTFT)的分类识别方法.首先对无人机的微多普勒回波信号进行SSTFT从而获得信号时频谱,然后对时频谱进行多维度特征提取获得回波信号的时频特征...     

基于AP聚类的数字信号调制体制识别方法

针对高斯噪声信道下MASK、MFSK和MPSK信号的类间识别问题,提出了一种基于短时傅里叶变换（Short Time Fourier Transform,STFT）和仿射传播聚类（Affinity Propagation Clustering,AP）相结合的信号类间识别方法。通过对在高斯信道下3类信号时域和频域特征的联合分析,提取出信号的时频特征。通过仿射传播聚类算法对信号进行聚类,通过信息迭代更新,可以快速、自动地找到聚类中心和聚类数目。仿真结果表明,在信噪比（SNR）较低的情况下仍能达到很好的分类效果。     

基于时频图深度学习的雷达动目标检测与分类

雷达动目标检测技术一直是雷达信号处理领域中的关键技术,而传统的雷达动目标检测技术仅适用于匀速运动目标,检测性能有限。针对该问题提出一种基于卷积神经网络(CNN)时频图处理的雷达动目标检测方法,通过从雷达动目标回波中提取多普勒频移信息,然后利用短时傅里叶变换转换为时频图,输入卷积神经网络,进行深度特征学习,进而实现检测和分类的目的。仿真数据验证表明,所提方法能够有效检测和区分匀速、匀变速运动以及微动目标,稳健性高,与传统动目标检测方法相比具有显著优势。     

Comparison of time-frequency distribution techniques for analysisof simulated Doppler ultrasound signals of the femoral artery

The time-frequency distribution of the Doppler ultrasound blood flow signal is normally computed by using the short-time Fourier transform or autoregressive modeling. These two techniques require stationarity of the signal during a finite interval. This requirement imposes some limitations on the distribution estimate. In the present study, three new techniques for nonstationary signal analysis (the Choi-Williams distribution, a reduced interference distribution, and the Bessel distribution) were tested to determine their advantages and limitations for analysis of the Doppler blood flow signal of the femoral artery. For the purpose of comparison, a model simulating the quadrature Doppler signal was developed, and the parameters of each technique were optimized based on the theoretical distribution. Distributions computed using these new techniques were assessed and compared with those computed using the short-time Fourier transform and autoregressive modeling. Three indexes, the correlation coefficient, the integrated squared error, and the normalized root-mean-squared error of the mean frequency waveform, were used to evaluate the performance of each technique. The results showed that the Bessel distribution performed the best, but the Choi-Williams distribution and autoregressive modeling are also techniques which can generate good time-frequency distributions of Doppler signals     

分步式短脉冲激光时间分布特征快速识别模型构建

短脉冲激光中时间分布特征会对光束调节产生一定影响,降低其超分辨率,为提高激光测量系统识别精度,构建分步式短脉冲激光时间分布特征快速识别模型。针对脉冲在传输过程中出现畸变的现象,利用傅里叶变换做时频信号转换,得出脉冲传输规律;通过对激光工作物质储能的调节,确保光源质量,采用CCD测量方法获取空间载波条纹图像,从条纹周期中获得脉冲相位分布信息;结合相位分布情况,使用多元线性表达式描述脉冲时间序列;对该序列做Hilbert转化,建立脉冲信号模型,获取脉冲包络特性,重建相位空间,得到时间特征总点数比值,经过对时频熵的计算,识别出时间分布特征,完成完整识别模型的构建。仿真结果表明,设计方法能有效抑制谐波干扰,提高识别精度。     

Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study

In this paper, we introduce the nonstationary signal analysis methods to analyze the myoelectric (ME) signals during dynamic contractions by estimating the time-dependent spectral moments. The time-frequency analysis methods including the short-time Fourier transform, the Wigner-Ville distribution, the Choi-Williams distribution, and the continuous wavelet transform were compared for estimation accuracy and precision on synthesized and real ME signals. It is found that the estimates provided by the continuous wavelet transform have better accuracy and precision than those obtained with the other time-frequency analysis methods on simulated data sets. In addition, ME signals from four subjects during three different tests (maximum static voluntary contraction, ramp contraction, and repeated isokinetic contractions) were also examined.     

A resolution comparison of several time-frequency representations

Two signal components are considered resolved in a time-frequency representation when two distinct peaks can be observed. The time-frequency resolution limit of two Gaussian components, alike except for their time and frequency centers, is determined for the Wigner distribution, the pseudo-Wigner distribution, the smoother Wigner distribution, the squared magnitude of the short-time Fourier transform, and the Choi-Williams distribution. The relative performance of the various distributions depends on the signal. The pseudo-Wigner distribution is best for signals of this class with only one frequency component at any one time, the Choi-Williams distribution is most attractive for signals in which all components have constant frequency content, and the matched filter short-time Fourier transform is best for signal components with significant frequency modulation. A relationship between the short-time Fourier transform and the cross-Wigner distribution is used to argue that, with a properly chosen window, the short-time Fourier transform of the cross-Wigner distribution must provide better signal component separation that the Wigner distribution