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13E053SAS - Spectral Signal Analysis

Course specification
Course title Spectral Signal Analysis
Acronym 13E053SAS
Study programme Electrical Engineering and Computing
Module
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
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
  1. Modern Spectral Estimation: Theory and Application, Steven Kay, Prentice Hall, 1998
  2. Signals, Systems, and Transforms, Charles Phillips, John Paar, Eve Riskih, Prentice Hall, 2003.
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
Colloquia 0
Seminars 0