Adaptive Short-Time Fractional Fourier Transform Based on Minimum Information Entropy

Traditional short-time fractional Fourier transform(STFrFT)has a single and fixed window function,which can not be adjusted adaptively according to the characteristics of fre-quency and frequency change rate.In order to overcome the shortcomings,the STFrFT method with adaptive window function is proposed.In this method,the window function of STFrFT is ad-aptively adjusted by establishing a library containing multiple window functions and taking the minimum information entropy as the criterion,so as to obtain a time-frequency distribution that better matches the desired signal.This method takes into account the time-frequency resolution characteristics of STFrFT and the excellent characteristics of adaptive adjustment to window func-tion,improves the time-frequency aggregation on the basis of eliminating cross term interference,and provides a new tool for improving the time-frequency analysis ability of complex modulated sig-nals.

Application of short-time Fourier transform to high-rise frame structural-health monitoring based on change of inherent frequency over time

The high-rise frame structure has become more and more widespread, like its damage from the complication of the environment. The traditional method of damage detection, which is only suitable for the stationary signal, does not apply to a high-rise frame structure because its damage signal is non-stationary. Thus, this paper presents an application of the short-time Fourier transform(STFT) to damage detection of high-rise frame structures. Compared with the fast Fourier transform, STFT is found to be able to express the frequency spectrum property of the time interval using the signal within this interval. Application of STFT to analyzing a Matlab model and the shaking table test with a twelve-story frame-structure model reveals that there is a positive correlation between the slope of the frequency versus time and the damage level. If the slope is equal to or greater than zero, the structure is not damaged. If the slope is smaller than zero, the structure is damaged, and the less the slope is, the more serious the damage is. The damage results from calculation based on the Matlab model are consistent with those from the shaking table test, demonstrating that STFT can be a reliable tool for the damage detection of high-rise frame structures.

Adaptive Fourier Decomposition Based Time-Frequency Analysis

The attempt to represent a signal simultaneously in time and frequency domains is full of challenges. The recently proposed adaptive Fourier decomposition （AFD） offers a practical approach to solve this problem. This paper presents the principles of the AFD based time-frequency analysis in three aspects： instantaneous frequency analysis, frequency spectrum analysis, and the spectrogram analysis. An experiment is conducted and compared with the Fourier transform in convergence rate and short-time Fourier transform in time-frequency distribution. The proposed approach performs better than both the Fourier transform and short-time Fourier transform.

FM interference suppression for PRC-CW radar based on adaptive STFT and time-varying filtering

The influence of frequency modulation （FM） interfer- ence on correlation detection performance of the pseudo random code continuous wave （PRC-CW） radar is analyzed. It is found that the correlation output deteriorates greatly when the FM inter- ference power exceeds the anti-jamming limit of the radar. Accord- ing to the fact that the PRC-CW radar echo is a wideband pseudo random signal occupying the whole TF plane, while the FM in- terference only concentrates in a small portion, a new method is proposed based on adaptive short-time Fourier transform （STFT） and time-varying filtering for FM interference suppression. This method filters the received signal by using a binary mask to excise only the portion of the TF plane corrupted by the interference. Two types of interference, linear FM （LFM） and sinusoidal FM （SFM）, under different signal-to-jamming ratio （S JR） are studied. It is shown that the proposed method can effectively suppress the FM interference and improve the performance of target detection.

Adaptive Noise Cancellation Method Used for Wheel Speed Signal of Integrate ABS/ASR System

A novel adaptive noise cancellation method for wheel speed signal of the anti-lock braking system/ anti-slip regulation（ABS/ASR） control system is proposed. Based on the spectrum distribution of vehicle＇s wheel speed signal got from fast Fourier transform under various conditions, the high-pass filter is used to deal with original wheel speed signals sampled to get reference noise signal and the original wheel speed signals are used as adaptive filter＇s desired outputs. The difference between original signals and reference noise signals is used as the error signal for the adaptive FIR filter and also used as the whole adaptive noise cancellation system＇s final output. This method can obtain the noise signal on-line and is easy to use for. real control system, which is useful to improve the performance of integrate system ABS/ASR.

Order selection in fractional Fourier transform based beamforming

Traditionally,beamforming using fractional Fourier transform（FrFT） involves a trial-and-error based FrFT order selection which is impractical.A new numerical order selection scheme is presented based on fractional power spectra（FrFT moment） of the linear chirp signal.This method can adaptively determine the optimum FrFT order by maximizing the second-order central FrFT moment.This makes the desired chirp signal substantially concentrated whereas the noise is rejected considerably.This improves the mean square error minimization beamformer by reducing effectively the signal-noise cross terms due to the finite data length de-correlation operation.Simulation results show that the new method works well under a wide range of signal to noise ratio and signal to interference ratio.

基于自适应Fourier分解-同步提取变换的机械故障诊断方法

传统的同步提取变换方法(synchronous extraction transform,SET)用于机械故障诊断时,要求多分量信号满足各相邻模态的瞬时频率差大于所选取的SET窗函数频率支撑范围的2倍,否则容易产生频率混叠,实际信号往往无法满足该条件。此外,信号受到噪声的影响,难以获得故障的瞬时频率。针对传统的同步提取变换在机械故障诊断中存在的不足,结合自适应Fourier分解(AFD)和同步提取变换的各自优点,提出了一种基于AFD-SET的机械故障诊断方法。该方法能够有效处理频率接近的非平稳信号,准确地表达信号的瞬时频率,并且具有很快的收敛速度。仿真结果表明,提出的方法能够有效解决传统SET方法中的频率混叠,并具有更高的时频聚集性。将提出的方法应用到滚动轴承故障诊断中,诊断结果表明,该算法正确有效,能够有效提取出故障信号的频率特征。

无监督域适应迁移学习在旋转机械故障诊断中的应用

故障诊断在旋转机械领域具有重要的意义,而深度学习和迁移学习的发展为提高故障诊断的准确性和鲁棒性提供了新的途径。针对旋转机械故障诊断问题,提出了一种基于深度对抗神经网络(domain-adversarial neural network, DANN)和多核最大平均差异(multiple kernel maximum mean discrepancy, MK-MMD)的无监督域适应迁移学习方法。首先,收集了源工况和目标工况下的振动信号数据并通过快速傅里叶变换(fast Fourier transform, FFT)转化为频域信号。然后,构建了一个ResNeXt-50特征提取器,并使用DANN和MK-MMD方法进行特征映射和域适应,从而实现源工况到目标工况的迁移学习。试验结果表明,该方法能提高对故障特征的识别精度,且在不同工况下的迁移试验中具有更好的鲁棒性。

基于FRFT的频域自适应匹配滤波器检测方法

针对浅海混响背景下频域自适应匹配滤波器检测性能下降的问题,提出基于分数阶傅里叶变换(FRFT)的频域自适应匹配滤波器检测方法,该方法利用模板匹配技术,采用滑动窗对接收信号进行最优阶次分数阶傅里叶变换,然后将此过程中得到的FRFT域图与参考信号最优阶次傅里叶变换FRFT域图进行匹配,将离差平方和作为评价相似度的指标,即对离差平方和最小值的位置进行滤波,并对滤波后的信号进行最优阶次分数阶傅里叶逆变换,从而实现混响背景下的目标检测。仿真结果表明,在信混比为-15dB的情况下,该算法可显著提高频域自适应匹配滤波器的检测性能。

分数阶Fourier变换域中网络流量的自相似特性分析

通过分析网络流量数据在FrFT域的统计特性发现,实际网络流量在FrFT域满足自相似性,进一步地,针对网络流量在FrFT域的"时域"和"频域"展开,分别给出了基于改进的整体经验模态分解—去趋势波动分析(MEEMD-DFA)的Hurst指数估计法以及基于加权最小二乘回归(WLSR)的Hurst指数自适应估计法。实验结果表明,相比于现有估值算法,MEEMD-DFA法具有较高的估计精度,但计算复杂度高;而FrFT自适应估计法则具有更优的估计顽健性,且计算复杂度较低,可作为一种实时在线估计真实网络数据Hurst指数的方法。

Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN

The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations occur.This paper proposes a subsynchronous oscillation sourcelocalization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ports.Next,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model.Ultimately,the trained CNN model realizes the online localization of subsynchronous oscillation sources.The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform.Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios.

基于自适应LMS算法的跨声速风洞模型系统辨识

针对风洞试验模型系统辨识不准确的问题,利用自适应LMS(least mean square)滤波器模型对跨声速风洞模型进行系统辨识。由于实测信号中存在多模态耦合,为了提高系统辨识精准度,首先对输入输出信号作了FRF(frequency response analysis)分析得到试验模型俯仰方向前两阶模态,其次利用快速Fourier变换进行模态解耦,接着利用自适应LMS滤波器模型、传递函数模型、多项式模型对俯仰方向单模态进行系统辨识,最后得到了基于自适应LMS滤波器模型的俯仰方向一阶、二阶模态滤波器系数。通过对比不同数学模型的输出与输入之间的相关系数和均方误差及辨识结果,表明自适应LMS滤波器模型具有更高的系统辨识精准度和更简洁的数学模型结构。为后续风洞试验模型振动主动控制计算法的设计提供有力支撑。

分数阶Fourier域非均匀采样Chirp信号的频谱研究

文中提出了一种自适应非均匀采样方法，并利用分数阶Fourier变换对这类非均匀采样信号在分数阶域的频谱进行了分析和研究，得到了这类非均匀采样信号在分数阶域的数字谱表达式；进一步得到了非均匀采样Chirp信号的分数阶频谱表达式；最后，通过仿真验证了结论。

分数阶Fourier域多分量微弱LFM信号的检测

为了提高对多分量微弱LFM信号的检测能力,提出了一种基于分数阶Fourier域非均匀采样理论的信号检测方法。首先提出了一种自适应非均匀采样方法,建立了该方法的模型,得到了非均匀采样LFM信号在分数阶Fourier域的频谱表达式。在此基础上,对多分量非均匀采样LFM信号进行了检测研究。计算机仿真结果表明,同传统的信号检测方法相比,该方法对微弱LFM信号具有较好的检测能力,而且减少了运算量,满足了实时性要求。

Radar Signal Intra-Pulse Feature Extraction Based on Improved Wavelet Transform Algorithm

With the new system radar put into practical use, the characteristics of complex radar signals are changing and developing. The traditional analysis method of one-dimensional transformation domain is no longer applicable to the modern radar signal processing, and it is necessary to seek new methods in the two-dimensional transformation domain. The time-frequency analysis method is the most widely used method in the two-dimensional transformation domain. In this paper, two typical time-frequency analysis methods of short-time Fourier transform and Wigner-Ville distribution are studied by analyzing the time-frequency transform of typical radar reconnaissance linear frequency modulation signal, aiming at the problem of low accuracy and sen-sitivity to the signal noise of common methods, the improved wavelet transform algorithm was proposed.

面向本科生的傅里叶变换拓展教学方法

在本科生电子信息类教学中,傅里叶变换占有非常重要的地位,在学习了连续和离散傅里叶变换后,作为教学的延伸,引入窗口傅里叶变换作为拓展教学内容,并启发式地引导学生对自适应傅里叶分解进行探究。主要介绍从傅里叶变换到窗口傅里叶变换(STFT)和自适应傅里叶分解(AFD)的信号分析拓展教学方法,以强化学生的理论基础,增强学生的应用能力,让学生了解更多的学术前沿研究成果,启发学生的创新性思维。

基于Gabor滤波的软包电池表面缺陷检测

针对当前锂锰软包电池表面在生产过程中容易出现凸点、凹点等缺陷的问题,提出了一种基于光度立体法与频域Gabor滤波相结合的电池表面缺陷检测方法。首先,采用3个光源采集图片,然后进行图像融合,生成电池样本的反照率图片,可以使缺陷更加明显;然后对反照率图片进行快速傅里叶变换,在频域状态下使用Gabor滤波器进行处理,处理的结果再用逆傅里叶变换转换到时域,对于去除拍摄反光引起的图片噪声,采用中值滤波去除,将缺陷部分根据灰度值的大小用一个矩形掩膜进行增强;然后采用全局自适应阈值分割提取缺陷区域;最后利用形态学操作细化缺陷,凹坑准确率在98%,凸点准确率在96.7%,能够达到实际工业应用要求。

基于CEEMDAN-小波包自适应阈值混凝土声发射信号降噪研究

为了得到更加纯净的混凝土声发射(acoustic emission, AE)信号来更准确地监测混凝土结构破裂过程,提出了一种完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition, CEEMDAN)与小波包自适应阈值联合方法对循环荷载作用下的混凝土声发射信号进行降噪处理,运用信噪比和快速傅里叶变化(fast Fourier transform, FFT)分析来验证所用方法的可行性。实验结果表明:结合CEEMDAN-小波包自适应阈值对混凝土声发射信号进行降噪的效果较好,能有效地保留混凝土声发射信号特征信息,对混凝土声发射信号降噪提供新的思路,为后续利用声发射信号分析混凝土结构内部微裂纹扩展及演化特征奠定基础。

基于canny算法的改进自适应边缘检测

传统边缘检测算法进行边缘检测时存在对图像噪声难以滤除和伪边缘的问题。为此,提出了基于自适应中值滤波和代数平均算法构成的混合滤波器,滤除大部分高斯噪声和椒盐噪声,结合二维快速傅里叶变换和高斯高通滤波消除图像伪边缘,并且在传统canny算子计算模板基础上增加了45°和135°方向的模板减小了漏检率,对分割阈值使用最大类间方差算法避免了人为确定阈值,提高算法整体的鲁棒性。将提出的新检测算法与多种现有算法进行视觉对比和定量评估,Matlab结果表明,所提出的边缘检测算法具有更好的检测性能,在多种噪声影响下仍然检测出精确结果。

基于自适应EMD-NDFT的太阳光谱多普勒红移测速方法

太阳是太阳系中唯一的能量源,拥有着极为宽阔的连续谱以及数以万计的吸收线和发射线,是一个非常丰富的光谱信息宝藏。太阳电磁辐射的能量主要集中在可见光区和红外光区,其中,具有多普勒红移特征的太阳红外光谱可作为天文测速导航的信息源。太阳光谱多普勒红移测速是天文测速导航的重要环节,它通过计算接收太阳光谱相对于标准太阳光谱的多普勒红移反推出航天器和太阳之间的相对径向速度。然而,太阳黑子、日冕、耀斑等太阳活动引发的光谱畸变会造成太阳光谱的不稳定,这将影响着太阳光谱的测速精度,进而影响导航精度。为了提高太阳光谱测速导航性能,依据太阳光谱测速原理,探索改进太阳光谱多普勒红移测速的信号处理方法。提出了一种面向太阳光谱测速导航的自适应EMD-NDFT多普勒红移测速方法,该方法针对太阳光谱的多普勒效应计算得到红移,进而反推得到航天器相对于光源的径向速度。该方法由EMD处理、NDFT变换、相关匹配三部分构成。即:首先运用EMD算法对非平稳的接收太阳光谱信号进行自适应分层,再根据每一层本征模态信号进行自适应阈值滤波降噪,以获得平稳的重构信号;然后根据太阳光谱非均匀采样的特点,对标准太阳光谱和接收光谱分别进行NDFT变换将光谱由时域转换到频域,再选择两者的低频特征谱线进行泰勒匹配以获得相位差,从而得到航天器相对于太阳的径向速度。该方法将信号的时域降噪和频域稀疏相结合,可更快速、更准确地得到径向速度。分析了太阳黑子活动的一个周期中,不同年份的光谱变化情况,并分别对其进行多普勒红移测速计算和分析。仿真实验结果表明,针对不同时间段和不同噪声下的太阳光谱数据,采用自适应EMD-NDFT方法可以有效提高测速精度,并能较大程度地降低计算复杂度。