频谱估计

周期图，Welch 和 Lomb-Scargle PSD，相干性，传递函数，频率重排

使用 periodogram、pwelch 或 plomb 分析均匀或非均匀采样信号的频谱内容。使用重排锐化周期图估计。确定信号之间的频域相干性。基于输入和输出测量值估计传递函数。研究频域中的 MIMO 系统。

App

可视化和比较多个信号和频谱

`cpsd`	Cross power spectral density
`findpeaks`	Find local maxima
`mscohere`	Magnitude-squared coherence
`pentropy`	Spectral entropy of signal
`periodogram`	Periodogram power spectral density estimate
`plomb`	Lomb-Scargle periodogram
`pmtm`	Multitaper power spectral density estimate
`poctave`	Generate octave spectrum
`pspectrum`	Analyze signals in the frequency and time-frequency domains
`pwelch`	Welch’s power spectral density estimate
`tfestimate`	Transfer function estimate

`db`	Convert energy or power measurements to decibels
`db2mag`	Convert decibels to magnitude
`db2pow`	Convert decibels to power
`mag2db`	Convert magnitude to decibels
`pow2db`	Convert power to decibels

Nonparametric Methods
Learn about the periodogram, modified periodogram, Welch, and multitaper methods of nonparametric spectral estimation.
Detect a Distorted Signal in Noise
Use frequency analysis to characterize a signal embedded in noise.
Detect Periodicity in a Signal with Missing Samples
Use the Lomb-Scargle periodogram to study the periodicity of an irregularly sampled signal.
测量信号的功率
估计包含信号大部分功率的频带宽度。对于失真信号，确定基波和谐波中存储的功率。
幅值估计和填零
通过填零获得正弦信号幅值的精确估计。
Bias and Variability in the Periodogram
Reduce bias and variability in the periodogram using windows and averaging.
比较两个信号的频率成分
识别频域中信号之间的相似性。
Significance Testing for Periodic Component
Assess the significance of a sinusoidal component in white noise using Fisher's g-statistic.
Find Periodicity in a Categorical Time Series
Perform spectral analysis of data whose values are not inherently numerical.
交叉频谱和幅值平方相干性
获取正弦分量之间的相位滞后，并识别时间序列中的频域相关性。
Nonparametric Spectrum Object to Function Replacement
Replace calls to nonparametric psd and msspectrum objects with function calls.