OS3SAS - Spectral Signal Analysis

Course specification
Course title		Spectral Signal Analysis
Acronym		OS3SAS
Study programme		Electrical Engineering and Computing
Module		Signals and Systems
Type of study		bachelor academic studies
Lecturer (for classes)		professor PhD Veljko Papić
Lecturer/Associate (for practice)		professor PhD Predrag Tadić professor PhD Aleksandra Krstić asst. professor PhD Sanja Vujnović
Lecturer/Associate (for OTC)		professor PhD Predrag Tadić professor PhD Aleksandra Krstić asst. professor PhD Sanja Vujnović
ESPB		6.0	Status	elective
Condition		Digital signal processing
The goal		Objective of the course is to inform the students about the methods for spectral signal analysis and parameter estimation. Also, the objective is to prepare the students these problems to be able to solve using programming language MATLAB.
The outcome		Learning outcomes of the course is for the students to gain the following skills: to apply different methods for power spectral density estimation, to recognize the form of the process, to make appropriate spectral analysis, to make a choice of the model order.
Contents
Contents of lectures		Stochastic processes. Classical methods for spectral analysis (periodogram, averaged periodoram, Blackman-Tukey method). Modern methods for spectral estimation. Modeling of the processes (AR, MA and ARMA models). Spectral estimation of AR models: Autocorrelation, Covariant, Modified Covariant, Burg's method. Selection of model order.
Contents of exercises		Students have obligation to solve independently three practical problems using the programming language MATLAB.
Literature
Modern Spectral Estimation: Theory and Application, Steven Kay, Prentice Hall, 1998 (Original title) Signals, Systems, and Transforms, Charles Phillips, John Paar, Eve Riskih, Prentice Hall, 2003. (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		40	Oral examination	60
Projects		0
Colloquia		0
Seminars		0