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

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估算器

`fsst`	Fourier synchrosqueezed transform
`ifsst`	Inverse Fourier synchrosqueezed transform
`instbw`	Estimate instantaneous bandwidth
`instfreq`	Estimate instantaneous frequency
`kurtogram`	Visualize spectral kurtosis
`pkurtosis`	Spectral kurtosis from signal or spectrogram
`pentropy`	Spectral entropy of signal
`pspectrum`	Analyze signals in the frequency and time-frequency domains
`spectrogram`	Spectrogram using short-time Fourier transform
`xspectrogram`	Cross-spectrogram using short-time Fourier transforms
`stft`	Short-time Fourier transform
`dlstft`	Deep learning short-time Fourier transform
`stftmag2sig`	Signal reconstruction from STFT magnitude
`iscola`	Determine whether window-overlap combination is COLA compliant
`istft`	Inverse short-time Fourier transform
`tfridge`	Time-frequency ridges
`wvd`	Wigner-Ville distribution and smoothed pseudo Wigner-Ville distribution
`xwvd`	Cross Wigner-Ville distribution and cross smoothed pseudo Wigner-Ville distribution

数据自适应方法

`emd`	Empirical mode decomposition
`vmd`	Variational mode decomposition
`hht`	Hilbert-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.