13E053SAS - Spectral Signal Analysis

Course specification
Course title		Spectral Signal Analysis
Acronym		13E053SAS
Study programme		Electrical Engineering and Computing
Module		Signals and Systems
Type of study		bachelor academic studies
Lecturer (for classes)		professor PhD Veljko Papić asst. professor PhD Predrag Tadić
Lecturer/Associate (for practice)		asst. professor PhD Predrag Tadić Maša Tiosavljević, electrical and computer engineer
Lecturer/Associate (for OTC)		asst. professor PhD Predrag Tadić Maša Tiosavljević, electrical and computer engineer
ESPB		6.0	Status	elective
Condition		Digital signal processing, Stochastic systems and estimation
The goal		Introduce students to basics methods for time-series analysis and spectral estimation. Enable students to practically implement and interpret the results of these algorithms using Matlab/Octave and Python.
The outcome		Students will understand theoretical and practical aspects of classical and parametric algorithms for estimating power spectra of wide-sense stationary stochastic signals. Students will be enabled to properly choose, practically implement and adequately tune the spectral estimation algorithms, and to interpret the results obtained by applying these methods to realistic signals.
Contents
URL to the subject page		https://automatika.etf.bg.ac.rs/index.php/sr/spektralna-analiza-signala-os3sas
URL to lectures		https://teams.microsoft.com/l/team/19%3AGs-R9JY3pc_pzFrEVAJAhff5b2G1fy5RCgSGxn25D4c1%40thread.tacv2/conversations?groupId=87ed92c2-489d-4311-a317-fe8134bbe31b&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba
Contents of lectures		Stochastic processes. Classical methods for spectral analysis: periodogram, Blackman-Tukey method. Parametric time-series modelling: AR, MA and ARMA models, linear prediction. Spectral estimation of AR models: Yule-Walker equations, Levinson-Durbin recursion, lattice filters, autocorrelation/(modified) covariance/Burg methods. Effect of measurement noise. Model order selection.
Contents of exercises		In class, with the teachers supervision and aid, the students will implement and verify methods covered in lectures. As a homework assignment, each student will be given samples of a signal, and their assignment will be to individually estimate and analyse the spectrum of the sampled signal.
Literature
Steven Kay, "Modern Spectral Estimation: Theory and Application", Prentice Hall, 1998 (Original title) Petre Stoica and Randolph L. Moses, "Spectral analysis of signals", Prentice Hall, 2005 (Original title)
Number of hours per week during the semester/trimester/year
Lectures	Exercises	OTC	Study and Research	Other classes
3	1	1
Methods of teaching		45 hours of lectures + 15 hours of auditory exercises + 15 hours of practical exercises with computers
Knowledge score (maximum points 100)
Pre obligations		Points	Final exam	Points
Activites during lectures		0	Test paper	0
Practical lessons		0	Oral examination	70
Projects		30
Colloquia		0
Seminars		0