GRADUATE SCHOOL

M.SC. in Electrical and Electronics Engineering (With Thesis)

EEE 542 | Course Introduction and Application Information

Course Name
Detection and Estimation Theory
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
EEE 542
Fall/Spring
3
0
3
7.5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
Second Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course -
Course Coordinator -
Course Lecturer(s)
Assistant(s) -
Course Objectives This course aims to provide a graduate-level introduction to detection and estimation theory. Course content includes the topics such as Gauss-Markov processes and stochastic differential equations, Bayes estimation theory, maximum likelihood, linear minimum deviation, minimum-squares estimation, properties of estimators, error analysis, state prediction for linear systems, Kalman-Bucy and Wiener filters, leveling and pre-estimation methods, nonlinear estimation, filtering applications, communications, control, system identification and biomedical engineering applications.
Learning Outcomes The students who succeeded in this course;
  • learn how to use and discuss the maximum likelihood, maximum a posteriori probability and least-squares estimates of a parameter,
  • learn how to perform Karhunen-Loeve expansion,
  • learn how to apply Wiener filter and Kalman filter to solve linear estimation problems,
  • be able to evaluate performance of decision making and estimation systems,
  • be able to design and implement various detection and estimation algorithms using Matlab simulation software.
Course Description Gauss-Markov processes and stochastic differential equations, Bayes estimation theory, maximum likelihood, linear minimum deviation, minimum-squares estimation, properties of estimators, error analysis, state prediction for linear systems, Kalman-Bucy and Wiener filters, leveling and pre-estimation methods, nonlinear estimation, filtering applications, communications, control, system identification and biomedical engineering applications.

 



Course Category

Core Courses
Major Area Courses
X
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction, Probability, Random Vectors, Vector Spaces Lecture notes
2 Detection Theory, Decision Theory, and Hypothesis Testing Lecture notes
3 Detection Theory, Decision Theory, and Hypothesis Testing Lecture notes
4 Detection Theory, Decision Theory, and Hypothesis Testing Lecture notes
5 Parameter Estimation Lecture notes
6 Maximum Likelihood Estimation Lecture notes
7 Stochastic Processes and Systems Lecture notes
8 Midterm
9 Karhunen-Loeve and Sampled Signal Expansions Lecture notes
10 Detection and Estimation from Waveform Observations Lecture notes
11 Wiener and Kalman Filtering Lecture notes
12 Advanced Topics Lecture notes
13 In-class Presentations
14 In-class Presentations
15 In-class Presentations
16 Review of the Semester  

 

Course Notes/Textbooks Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory, Steven Kay, 1993. ISBN 0-13-345711-7 Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, Steven Kay, 1998. ISBN 0-13-504135-X
Suggested Readings/Materials Related Research Papers

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
5
30
Presentation / Jury
Project
1
30
Seminar / Workshop
Oral Exams
Midterm
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
6
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
15
4
60
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
5
10
50
Presentation / Jury
0
Project
1
45
45
Seminar / Workshop
0
Oral Exam
0
Midterms
0
Final Exam
1
22
22
    Total
225

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Accesses information in breadth and depth by conducting scientific research in Electrical and Electronics Engineering; evaluates, interprets and applies information X
2 Is well-informed about contemporary techniques and methods used in Electrical and Electronics Engineering and their limitations X
3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines X
4 Is informed about new and upcoming applications in the field and learns them whenever necessary.

X
5 Defines and formulates problems related to Electrical and Electronics Engineering, develops methods to solve them and uses progressive methods in solutions. X
6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs. X
7 Designs and implements studies based on theory, experiments and modeling; analyses and resolves the complex problems that arise in this process. X
8 Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility. X
9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale. X
10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. X
11 Is knowledgeable about the social, environmental, health, security and law implications of Electrical and Electronics Engineering applications, knows their project management and business applications, and is aware of their limitations in Electrical and Electronics Engineering applications. X
12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. Adheres to the principles of research and publication ethics.
X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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