多分量非平稳声信号时频分析

为了准确提取信号所包含的主要频率分量,对多分量非平稳声信号进行了时频分析。利用短时傅立叶变换将多分量非平稳声信号由时域变换到时频域,根据谱图提取信号的主要频率分量。分析结果表明：多分量非平稳信号的各主要频率分量及其时频域特性参数可以准确提取。短时傅立叶变换是提取多分量非平稳声信号主要频率分量的有效方法。     

非平稳短时矢谱研究及其应用

大型旋转机械的故障在发生或发展时,其运行过程具有明显非平稳特性的瞬态过程。通常的针对单通道信息的时频分析不能准确提取瞬态过程的特征信息。论文结合矢谱分析和短时傅立叶变换（STFT）技术提出了短时矢谱（STVS）分析方法。它融合了转子同截面两通道图谱中各自存在的振动分量,能准确地反映转子发生故障时的瞬态过程特征量变化。工程实践应用表明：短时矢谱分析对于旋转机械故障诊断是一种新的、较为实用的信息融合方法。     

基于时-频分析的语音盲分离算法

在时-频分析算法中,窗长的选择对分离语音信号的性能有着重要的影响。文章详细介绍了基于时一频分析的语音盲分离算法,并用MATLAB仿真分析了不同窗函数、窗长的选择对分离信号性能的影响.实验证明,对于语音这种短时平稳信号,用时-频分析盲分离算法分离混合语音信号时,窗长选择为256时可取得最佳的分离效果。     

基于STFT填埋场衬层修补技术中振动信号分析

钻探灌浆修补技术是填埋场防渗层漏洞修补问题.为了准确控制钻头到达卵石层且不能击穿防渗层,根据不同介质层振动信号响应不同来判断卵石层,波形时域分析方法能区分卵石层但对钻头要求较高,实际应用中存在局限.采用短时傅里叶(STET)时频分析方法在牙钻条件下对三种介质模型中的振动信号进行分析,利用相应频带的能量突变点来区分卵石层,通过选用合适的窗函数和窗长提取不同介质层信号进行仿真.实验结果表明,STET能有效区分卵石层和其它介质层,具有很高的准确率.     

信号的小波分析及在信号检测中的应用

对典型的线性调频信号进行了小波分析,并运用小波变换方法对噪声中的方波信号和正弦信号进行了检测,讨论了小波变换滤波器参数变化对检测性能的影响,最后,将小波变换与短时傅里叶变换进行了比较。     

基于时频分布的跳频信号分析研究

利用时频分布中的三种不同的方法--短时傅立叶变换、小波变换和Wigner-Ville分布进行跳频信号分析,通过理论研究和仿真分析表明,三种方法均可以较好的展示跳频信号的时频特征,同时分析出各自的优缺点,进而证明了每种方法应用于跳频信号分析研究工程应用的可行性.     

STFT在航空发动机振动信号处理中的应用

当航空发动机工作于非平稳状态时,常规频谱分析和功率谱分析方法不能反映振动数据频谱随时间的变化情况。短时傅里叶变换(STFT)技术作为最为常用的时频分析技术之一,在工程应用领域具有算法简单、物理概念明确等优点,被广泛采用。鉴于STFT技术的上述优点,从航空发动机非平稳状态整机振动数据处理应用需求出发,对如何将短时傅里叶变换技术应用于航空发动机非平稳状态整机振动信号进行了分析研究,并给出了针对性的实现算法。     

多速率STFT超宽带信号瞬时频率估计研究

提出多速率短时傅里叶变换(Multi Rate Short Time Fourier Transform,MR-STFT)瞬时频率估计算法,提高了超宽带信号瞬时频率估计精度;该方法将多速率信号处理算法与短时傅里叶变换(STFT)技术相结合,兼顾采样频率和被测频率,将宽频范围进行分段采样,对分段处理结果进行拟合,构成多速率STFT算法,实现超宽带信号瞬时频率的高精度测量;通过对仿真信号和实测信号进行处理,研究了方法的可行性和频率估计精度,结果表明MR-STFT算法较大提高了超宽带信号瞬时频率估计精度,尤其对低信噪比的超宽带信号效果显著。     

基于SWT和FRFT的脉冲星TOA估计算法

提出了一种基于平稳小波和分数阶傅立叶变换的脉冲星超分辨率TOA估计算法。首先,对一个周期内的脉冲星信号进行多层平稳小波分解。然后,估计出与各层低频系数分数阶傅立叶变换相对应的TOA。最后,对脉冲星信号到达时间进行复合估计。实验结果表明,在低采样率,强噪声背景的情况下,该算法能获得稳定、准确的脉冲信号到达时间。     

类GPS超声定位系统及其多基站距离估计算法研究

针对常规超声波局部定位系统的缺陷,介绍了类GPS超声定位系统及其工作原理.建立了超声扩频测距时延估计模型,根据此模型提出了一种基于FFT的单基站距离估计快速算法.根据多基站接收信号的特点,在单基站距离估计算法的基础上,介绍了一种基于短时傅立叶变换(STFT)的多基站距离估计算法.实验研究表明,在获得发射信号载波频率先验知识的情况下,多基站距离估计算法运算量不大,并能精确得到距离估计值,从而为小车的精确定位奠定基础.     

混沌背景中微弱网络传输信号有效检测仿真

针对传统微弱信号检测方法存在检测准确性较低、检测时间较长等缺点,提出混沌背景中微弱网络传输信号有效检测方法。根据Wiener-Khinchine定理对信号函数计算,并经过傅里叶变换获得信号的自相关函数与其相对应的功率谱,利用周期图法分析获得微弱信号的检测原理,运用Briot-Bouquet引理进行计算,根据计算结果证明瞬态信号以及周期信号的存在,并以此为基础构建混沌背景下噪声的检测模型,从预测误差中检测出在噪声层下的微弱网络传输信号。仿真结果表明,所提方法能够快速检测出在混沌背景下的微弱信号,且检测时间较短、检测误差较低,可广泛引用在现实生活中。     

Recursive sliding discrete Fourier transform with oversampled data

The Discrete Fourier Transform (DFT) has played a fundamental role for signal analysis. A common application is, for example, an FFT to compute a spectral decomposition, in a block by block fashion. However, using a recursive, discrete, Fourier transform technique enables sample-by-sample updating, which, in turn, allows for the computation of a fine time–frequency resolution. An existing spectral output is updated in a sample-by-sample fashion using a combination of the Fourier time shift property and the difference between the most recent input sample and outgoing sample when using a window of finite length. To maintain sampling-to-processing synchronisation, a sampling constraint is enforced on the front–end hardware, as the processing latency per input sample will determine the maximum sampling rate. This work takes the recursive approach one step further, and enables the processing of multiple samples acquired through oversampling, to update the spectral output. This work shows that it is possible to compute a fine-grained spectral decomposition while increasing usable signal bandwidths through higher sampling rates. Results show that processing overhead increases sub-linearly, with signal bandwidth improvement factors of up to 6.7× when processing 8 samples per iteration.     

Representation of the Fourier Transform by Fourier Series

The analysis of the mathematical structure of the integral Fourier transform shows that the transform can be split and represented by certain sets of frequencies as coefficients of Fourier series of periodic functions in the interval . In this paper we describe such periodic functions for the one- and two-dimensional Fourier transforms. The approximation of the inverse Fourier transform by periodic functions is described. The application of the new representation is considered for the discrete Fourier transform, when the transform is split into a set of short and separable 1-D transforms, and the discrete signal is represented as a set of short signals. Properties of such representation, which is called the paired representation, are considered and the basis paired functions are described. An effective application of new forms of representation of a two-dimensional image by splitting-signals is described for image enhancement. It is shown that by processing only one splitting-signal, one can achieve an enhancement that may exceed results of traditional methods of image enhancement.     

Research progress on discretization of fractional Fourier transform

As the fractional Fourier transform has attracted a considerable amount of attention in the area of optics and signal processing, the discretization of the fractional Fourier transform becomes vital for the application of the fractional Fourier transform. Since the discretization of the fractional Fourier transform cannot be obtained by directly sampling in time domain and the fractional Fourier domain, the discretization of the fractional Fourier transform has been investigated recently. A summary of discretizations of the fractional Fourier transform developed in the last nearly two decades is presented in this paper. The discretizations include sampling in the fractional Fourier domain, discrete-time fractional Fourier transform, fractional Fourier series, discrete fractional Fourier transform （including 3 main types： linear combination-type; sampling-type; and eigen decomposition-type）, and other discrete fractional signal transform. It is hoped to offer a doorstep for the readers who are interested in the fractional Fourier transform.     

A new music-empirical wavelet transform methodology for time–frequency analysis of noisy nonlinear and non-stationary signals

The goal of signal processing is to estimate the contained frequencies and extract subtle changes in the signals. In this paper, a new adaptive multiple signal classification-empirical wavelet transform (MUSIC-EWT) methodology is presented for accurate time–frequency representation of noisy non-stationary and nonlinear signals. It uses the MUSIC algorithm to estimate the contained frequencies in the signal and build the appropriate boundaries to create the wavelet filter bank. Then, the EWT decomposes the time-series signal into a set of frequency bands according to the estimated boundaries. Finally, the Hilbert transform is applied to observe the evolution of calculated frequency bands over time. The usefulness and effectiveness of the proposed methodology are validated using two simulated signals and an ECG signal obtained experimentally. The results demonstrate clearly that the proposed methodology is immune to noise and capable of estimating the optimal boundaries to isolate the frequencies from noise and estimate the main frequencies with high accuracy, especially the closely-spaced frequencies.     

便携式四通道肺音采集系统设计

首先完成对肺音信号的放大、滤波等预处理;然后对预处理的肺音信号进行外部A/D采样,并将采集肺音信号保存为。 WAV音频文件存储于SD卡中;利用短时傅立叶变换完成了对肺音信号的时频域分析。通过该系统可以准确地检测到病人的肺音信号,并且利用立体声耳机可以实现对病人的同步听诊。     

基于全相位频谱校正的TMBOC信号的捕获

针对使用传统部分匹配滤波器（PMF）结合快速傅里叶变换（FFT）无法精确捕获时分复用二进制偏移载波（TMBOC）调制信号的问题,提出一种基于全相位频谱校正的捕获方法。首先通过PMF过程对接收信号进行部分相关运算,再使用全相位快速傅里叶变换（apFFT）算法对多普勒效应进行补偿,最后结合全相位频谱校正技术对功率谱进行校正。仿真结果表明,在同一条件下,该算法比PMF-FFT加窗算法检测概率提高了3 dB左右,并有效缩短了捕获时间。该算法可比PMF-FFT加窗算法更精确捕获TMBOC信号。     

基于深度卷积神经网络的变换域通信网络抗干扰优化算法

为了有效抑制变换域通信网络干扰信号,改善信噪比,研究了基于深度卷积神经网络的变换域通信网络抗干扰优化算法。应用傅里叶变换方法将信号从时域转换到频域,并以傅里叶变换通信信号获得的参数为依据构建干扰信号模型;嵌入干扰信号模型以形成接收信号,然后对接收信号进行处理并存储在干扰数据库中,利用深度卷积神经网络完成干扰信号的特征学习与干扰估计,并根据干扰估计结果,在接收信号中去除干扰信号,完成变换域通信网络抗干扰优化。实验结果表明：该算法可有效完成变换域通信网络抗干扰优化,优化后通信信号的信噪比改善性能与误码性能均较佳,输出的通信信号几乎无干扰信号存在。     

基于小波的信号突变点检测算法研究

本文利用小波多分辨分析的特性将突变信号进行多尺度分解，然后通过分解后的信号来确定突变信号的突变位置。Lipschitz指数被用来定量描述函数的奇异性。当小波变换尺度越来越精细时，小波变换模极大值信号突变点的衰减速度取决于信号在突变点的Lipschitz指数。小波变换不仅可以确定突变点发生的时间，而且可以进一步判断突变的性质。