Statistical Spectrum and Frequency Estimation Examples

Examples from the M. Hayes' famous book "Statistical Digital Signal Processing and Modeling".
Updated 21 Dec 2017

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Some important classical (non-parametric) and modern (parametric) statistical spectrum and frequency estimation algorithms are demonstrated, reproducing the examples from chapter 8 of M. Hayes book. Namely, the following Methods are exposed:
A) Non-parametric Methods.
i) The Periodogram.
ii) Barlett's Method: Periodogram Averaging.
iii) Welch's Method: Averaging Modified Periodograms.
iv) Blackman-Tukey Method: Periodogram Smoothing.
B) Parametric Methods.
i) The Autocorrelation Method.
ii) The Covariance Method.
iii) The Modified Covariance Method.
iv) The Burg Algorithm.

C) Frequency Estimation.
i) Pisarenko Harmonic Decomposition (PHD).
ii) Multiple Signal Classification (MUSIC).
iii) The Eigenvector Method.
iv) The Minimum Norm Algorithm.

Cite As

Ilias Konsoulas (2024). Statistical Spectrum and Frequency Estimation Examples (, MATLAB Central File Exchange. Retrieved .

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Created with R2011b
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Version Published Release Notes

Corrected some x-axis inconsistencies. No all x-axis frequency variables are in units of pi.

I have updated the link to M. Hayes .m scripts necessary to run these examples.
I improved the appearance of code and figure rendering.