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Statistical Spectrum and Frequency Estimation Examples

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



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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.

Comments and Ratings (3)

Nam Hoang Le

and a lot of functions I can not find in your examples. please show? Thank u

Ilias Konsoulas

@ Michael: As stated in the
"Other Requirements" section of this page, you need to download the book's companion software from:

the music function used - where do I find this?



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


I improved the appearance of code and figure rendering.

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