# Spectral Estimation

Periodogram, Welch, and Lomb-Scargle PSD, coherence, transfer function, frequency reassignment

Analyze the spectral content of uniformly or nonuniformly sampled signals using `periodogram`, `pwelch`, or `plomb`. Sharpen periodogram estimates using reassignment. Determine frequency-domain coherence between signals. Estimate transfer functions based on input and output measurements. Study MIMO systems in the frequency domain.

## Apps

 Signal Analyzer Visualize and compare multiple signals and spectra

## Functions

expand all

 `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

## Topics

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.

Measure the Power of a Signal

Estimate the width of the frequency band that contains most of the power of a signal. For distorted signals, determine the power stored in the fundamental and the harmonics.

Obtain an accurate estimate of the amplitude of a sinusoidal signal using zero padding.

Bias and Variability in the Periodogram

Reduce bias and variability in the periodogram using windows and averaging.

Compare the Frequency Content of Two Signals

Identify similarity between signals in the frequency domain.

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.

Cross Spectrum and Magnitude-Squared Coherence

Obtain the phase lag between sinusoidal components and identify frequency-domain correlation in a time series.

Nonparametric Spectrum Object to Function Replacement

Replace calls to nonparametric `psd` and `msspectrum` objects with function calls.