Learn more about Signal Processing Toolbox

Introduction

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Spectral Analysis Examples Spectral Estimators Spectral Analysis Algorithms Spectral Analysis Objects

Spectral Analysis Examples

The first example shows you how to compute and display a mean-square spectrum. The second example shows you how to measure the average power of a signal.

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Spectral Estimators

Spectral analysis includes three types of spectral estimators — power spectral density (PSD), mean-square spectrum (MSS) and pseudo spectrum.

• Power spectral density (psd) measures power per unit of frequency and has power/frequency units.

• Mean-square (power) spectrum (msspectrum) measures power at a specific frequency.

• Pseudospectrum (pseudospectrum) returns a pseudo spectrum that does not have any units.

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Spectral Analysis Algorithms

Signal Processing Toolbox software provides several algorithms to compute of spectral estimates. The following table indicates which algorithms are available to compute each type of estimator and produce a spectrum object. For general information on objects, see Object-Oriented Programming. For details on spectrum objects, see the spectrum reference page.

Spectral Estimator	Algorithms
Power spectral density (psd)	Burg (spectrum.burg), Covariance (spectrum.cov), Modified covariance (spectrum.mcov), Thomson multitaper method (MTM) (spectrum.mtm), Periodogram (spectrum.periodogram), Welch (spectrum.welch), Yule-Walker autoregressive (spectrum.yulear) See also dspdata.psd
Mean-square spectrum (msspectrum)	Periodogram (spectrum.periodogram), Welch (spectrum.welch) See also dspdata.msspectrum
Pseudo spectrum (pseudospectrum)	Eigenvector (spectrum.eigenvector), MUSIC (Multiple Signal Classification) (spectrum.music) See also dspdata.pseudospectrum

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Spectral Analysis Objects

Spectral analysis objects contain property values for the particular algorithm. To calculate a spectrum estimate, you first create an estimator object using one of the algorithms (h = spectrum.burg). You then pass your data and the estimator object to a spectrum estimation algorithm (Hpsd = psd(h,x)). In this example, h is a Burg spectrum object, x is the original input data, and Hpsd is the resulting PSD estimate.

For more information and examples, see the Getting Started with Spectral Analysis Objects demo.

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Spectral Analysis

Creating a Spectral Analysis Object

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