Main Content

时频分析

频谱图、同步压缩、重排、Wigner-Ville、时频边缘、数据自适应方法

Signal Processing Toolbox™ 提供的函数和 App 可用于可视化和比较非平稳信号的时频内容。计算短时傅里叶变换及其逆变换。使用重排或傅里叶同步压缩获得清晰的频谱估计。绘制交叉频谱图、Wigner-Ville 分布和持久频谱。提取并跟踪时频脊。估计瞬时频率、瞬时带宽、谱峭度和谱熵。使用经验或变分模态分解和 Hilbert-Huang 变换执行数据自适应时频分析。

App

信号分析器Visualize and compare multiple signals and spectra
信号标注器Label signal attributes, regions, and points of interest

函数

全部展开

fsstFourier synchrosqueezed transform
ifsstInverse Fourier synchrosqueezed transform
instbwEstimate instantaneous bandwidth
instfreqEstimate instantaneous frequency
kurtogramVisualize spectral kurtosis
pkurtosisSpectral kurtosis from signal or spectrogram
pentropySpectral entropy of signal
pspectrumAnalyze signals in the frequency and time-frequency domains
spectrogramSpectrogram using short-time Fourier transform
xspectrogramCross-spectrogram using short-time Fourier transforms
stftShort-time Fourier transform
dlstftDeep learning short-time Fourier transform
stftmag2sigSignal reconstruction from STFT magnitude
iscolaDetermine whether window-overlap combination is COLA compliant
istftInverse short-time Fourier transform
tfridgeTime-frequency ridges
wvdWigner-Ville distribution and smoothed pseudo Wigner-Ville distribution
xwvdCross Wigner-Ville distribution and cross smoothed pseudo Wigner-Ville distribution
emdEmpirical mode decomposition
vmdVariational mode decomposition
hhtHilbert-Huang transform

主题

Time-Frequency Gallery

Examine the features and limitations of the time-frequency analysis functions provided by Signal Processing Toolbox.

Practical Introduction to Continuous Wavelet Analysis (Wavelet Toolbox)

This example shows how to perform and interpret continuous wavelet analysis.

基于 FFT 的时频分析

显示线性 FM 信号的频谱图。

Instantaneous Frequency of Complex Chirp

Compute the instantaneous frequency of a signal using the Fourier synchrosqueezed transform.

Detect Closely Spaced Sinusoids

Compute the instantaneous frequency of two sinusoids using the Fourier synchrosqueezed transform. Determine how separated the sinusoids must be for the transform to resolve them.

Radar and Communications Waveform Classification Using Deep Learning (Phased Array System Toolbox)

This example shows how to classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox)

Classify pedestrians and bicyclists based on their micro-Doppler characteristics using a deep learning network and time-frequency analysis.

特色示例