Text: DIGITAL SPECTRAL ANALYSIS WITH APPLICATIONS, by S.L. Marple, Jr.

Prerequisites: EEE407 and EEE350

Objective: The goal of this course is to introduce graduate students to the principles and applications of spectral estimation.

Outline:

Review of matrix theory and random processes, Response of linear discrete systems to random inputs, Deterministic spectral analysis, The sample spectrum Autocorrelation and Crosscorrelation estimates, Periodograms and Correlograms AR, MA, and ARMA models, Pade approximations, Yule-Walker equations Linear prediction and lattice structures, Eigenanalysis Methods, The MUSIC Algorithm.
 

Course Coordinator: Andreas Spanias

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