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Introduction

Spectral Estimators

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

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 EstimatorAlgorithms
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

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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