频谱估计

周期图，韦尔奇和隆布-斯卡格尔 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`	查找局部最大值
`mscohere`	Magnitude-squared coherence
`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
`refinepeaks`	Refine peak value and location estimates (自 R2024b 起)
`spectralEntropy`	Spectral entropy for signals and spectrograms
`tfestimate`	Transfer function estimate

`resize`	Resize data by adding or removing elements (自 R2023b 起)
`paddata`	Pad data by adding elements (自 R2023b 起)
`trimdata`	Trim data by removing elements (自 R2023b 起)

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.
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。
振幅估计和填零
通过填零获得正弦信号振幅的精确估计。
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。
使用频率分析求周期性
频谱分析有助于表征数据中的振荡行为，并测量不同周期。
Significance Testing for Periodic Component
Assess the significance of a sinusoidal component in white noise using Fisher's g-statistic.
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。
Price Weather Derivatives (Financial Instruments Toolbox)
This example demonstrates a workflow for pricing weather derivatives based on historically observed temperature data.