Detection and Estimation (EE611) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Detection and Estimation EE611 3 0 0 3 5
Pre-requisite Course(s)
The prerequisites for this course; Digital Signal Processing (EE306), Probability and Random Processes (EE213), MATLAB and basic computer programming skills.
Course Language English
Course Type N/A
Course Level Ph.D.
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Question and Answer, Drill and Practice.
Course Coordinator
Course Lecturer(s)
  • N/A
Course Assistants
Course Objectives Understanding detection theory; binary hypothesis testing, M-ary testing, Bayes and Neyman-Pearson detectors, min-max. theory, Understanding estimation theory; linear and nonlinear estimation, parameter estimation, MAP and maximum likelihood estimators, Cramér-Rao bounds, asymptotic properties of estimators, waveform detection and estimation, Wiener filtering and Kalman-Bucy filtering, spectral estimation, and important research topics for Ph.D. work.
Course Learning Outcomes The students who succeeded in this course;
  • apply detection techniques and find the wanted signals (either deterministic or random).
  • develop computational skills (Matlab) in signal detection problems.
  • apply estimation techniques and estimate the parameters of interest.
  • expose students to applications of real-world detection and estimation problems.
Course Content Neyman-Pearson detector, hypothesis testing, maximum likelihood estimator, MAP, Kalman filtering, Wiener filtering, detection and estimation performance evaluation.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Course overview and introduction. Introduction to hypothesis testing
2 Bayesian hypothesis testing
3 Min-max hypothesis testing
4 Neyman-Pearson and composite hypothesis testing
5 Detection of deterministic signals
6 Detection of signals with random parameters and stochastic signals
7 Performance evaluation of signal detection procedures
8 MIDTERM EXAM
9 Introduction to parameter estimation
10 Bayesian parameter estimation
11 Maximum likelihood estimation
12 Signal estimation: Kalman-Bucy filtering
13 Wiener filtering
14 Performance evaluation of estimation procedures
15 Selected applications (reviewing research papers)
16 Selected applications (reviewing research papers)

Sources

Course Book 1. Detection, Estimation and Modulation Theory Part I: Detection, Estimation and Filtering Theory, 2nd Edition Harry L. Van Trees,Kristine L. Bell, Zhi Tian, 2013.
2. H. V. Poor, "An Introduction to Signal Detection and Estimation", Springer, 2/e, 1998.
3. • S. M. Kay, "Fundamentals of Statistical Signal Processing: Estimation Theory", Prentice Hall PTR, 1993.
4. • S. M. Kay, "Fundamentals of Statistical Signal Processing: Detection Theory", Prentice Hall PTR, 1998.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 5 25
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 35
Final Exam/Final Jury 1 40
Toplam 7 100
Percentage of Semester Work
Percentage of Final Work 100
Total 100

Course Category

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

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 5 80
Presentation/Seminar Prepration
Project
Report
Homework Assignments 5 6 30
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 4 4
Prepration of Final Exams/Final Jury 1 5 5
Total Workload 167