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.
All the examples and demos run perfectly on my machine, Windows 7, Home Premium and MatLab R2015b.
The link posted by Ilias Konsoulas no longer works. You can find the functions on this site here:
They do not work in modern matlab. There are some typos (N's need to be changed to M's) and some semicolons need to be removed form the end, for, and if functions. I will post a link later with the updated code when I get them all cleaned.
and a lot of functions I can not find in your examples. please show? Thank u
@ Michael: As stated in the
"Other Requirements" section of this page, you need to download the book's companion software from: http://users.ece.gatech.edu/~mhayes/stat_dsp/matlab.html
the music function used - where do I find this?
I have updated the link to M. Hayes .m scripts necessary to run these examples.
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.
Inspired by: Statistical Digital Signal Processing and Modeling
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