Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1EEE 502Statistical Signal Processing and Modelling3+0+03618.06.2026

 
Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program ELECTRICAL AND ELECTRONICS ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The aim of this course is to teach students the statistical properties of random signals, stochastic processes, and statistical signal processing methods. Within the scope of the course, students are expected to gain knowledge of random signal modeling, correlation and spectral analysis, linear estimation, Wiener filtering, parametric modeling, AR/MA/ARMA models, estimation methods, and basic decision-making approaches. The course also aims to enable students to apply statistical signal processing techniques to data obtained from communication systems, biomedical signals, radar, image processing, and engineering systems.
Course Content Introduction to random variables and stochastic processes; characterization of random signals using mean, variance, autocorrelation, and power spectral density; random signals through linear systems; spectral analysis; linear estimation; Wiener filtering; least squares method; parametric signal modeling; AR, MA, and ARMA models; model order selection; introduction to estimation theory; maximum likelihood estimation; basics of signal detection and decision-making problems; applications of statistical signal processing methods in engineering problems.
Course Methods and Techniques The course is conducted through lectures, problem solving, mathematical modeling, applications on sample signals, computer-based analysis, and homework/project assignments. Classroom discussions, algorithm derivations, MATLAB/Python-based applications, and analysis studies on real or sample datasets are used to help students understand statistical signal processing methods.
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Prof.Dr. Ergun Erçelebi ercelebi@gantep.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Hayes, M. H. (1996). Statistical Digital Signal Processing and Modeling. John Wiley & Sons.
Probability and Statistics, Signals and Systems, Digital Signal Processing, Linear Algebra, Numerical Methods.
Kay, S. M. (1993). Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory. Prentice Hall.
Course Notes Weekly lecture notes, study questions, application documents, and relevant chapters of the reference books.

Course Category
Mathematics and Basic Sciences %30
Engineering %50
Engineering Design %10
Field %10

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 30
Assignment 5 % 20
Project 1 % 20
Final examination 1 % 30
Total
8
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Weekly lecture hours 14 3 42
Reading Activities 14 2 28
Internet browsing, library work 6 3 18
Material design, application 6 4 24
Report preparation 1 12 12
Presentation preparation 1 6 6
Presentation 1 1 1
Midterm and midterm exam preparation 1 20 20
Final exam and preparation for the final exam 1 29 29
Total Work Load   Number of ECTS Credits 6 180

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explain the basic statistical properties of random variables, stochastic processes, and random signals.
2 Analyze random signals using mean, variance, autocorrelation, cross-correlation, and power spectral density.
3 Evaluate the time-domain and frequency-domain behavior of random signals passing through linear systems.
4 Apply spectral analysis, linear estimation, and Wiener filtering methods to signal processing problems.
5 Construct parametric signal models such as AR, MA, and ARMA and determine their model parameters.
6 Solve signal modelling problems using least squares, maximum likelihood, and basic estimation methods.
7 Apply statistical signal processing methods to engineering problems such as communication systems, biomedical signals, radar, image processing, or similar areas, and interpret the obtained results.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to the course and application areas of statistical signal processing Reviewing the course syllabus Lecture notes and the relevant chapter of the reference book
2 Random variables and fundamentals of probability Reviewing probability concepts Lecture notes and study questions
3 Stochastic processes and random signals Reviewing random variables Lecture notes and the relevant chapter of the reference book
4 Mean, variance, autocorrelation and cross-correlation Reviewing correlation concepts Lecture notes and application document
5 Power spectral density and spectral analysis Reviewing Fourier transform Lecture notes and study questions
6 Random signals through linear systems Reviewing LTI systems Lecture notes and the relevant chapter of the reference book
7 Linear estimation and Wiener filtering Reviewing estimation concepts Lecture notes and application document
8 Midterm exam and general review of previous topics Preparation for the midterm exam Lecture notes and study questions
9 Least squares method and introduction to adaptive filtering Reviewing matrix operations Lecture notes and the relevant chapter of the reference book
10 Parametric signal modeling Reviewing modeling concepts Lecture notes and application document
11 AR, MA, and ARMA models Reviewing time-series models Lecture notes and study questions
12 Model order selection and parameter estimation Reviewing parametric modeling Lecture notes and the relevant chapter of the reference book
13 Maximum likelihood estimation and basic detection problems Reviewing estimation methods Lecture notes and application document
14 Engineering applications, project presentations and general review Preparing the project report and presentation Lecture notes and project documents

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4
All 4 4 3 3
C1 4 2 1 1
C2 4 4 2 2
C3 4 4 2 2
C4 5 4 3 3
C5 5 4 3 3
C6 5 5 3 3
C7 4 5 4 4

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