ACADEMICS
Course Details

ELE636 - Detection and Estimation Theory

2022-2023 Fall term information
The course is not open this term
ELE636 - Detection and Estimation Theory
Program Theoretýcal hours Practical hours Local credit ECTS credit
MS 3 0 3 8
Obligation : Elective
Prerequisite courses : -
Concurrent courses : -
Delivery modes : Face-to-Face
Learning and teaching strategies : Lecture, Question and Answer, Problem Solving
Course objective : The objective of the course is to provide a good understanding of detection and estimation theory which represents a combination of the classical techniques of statistical inference and the random process characterization of communication, radar, sonar, and other modern data processing systems
Learning outcomes : State Binary and M-ary Hypotheses Testing Evaluate the performance of decision making and estimation systems Derive Cramer-Rao bound Find the maximum likelihood, maximum a posteriori probability and least squares estimates of a parameter Perform Karhunen-Loeve expansion
Course content : Classical Detection and Estimation Theory : - Binary Hypothesis Testing - Optimum Decision Criteria : Bayes, Neyman-Pearson, Minimax - Decision Performance : Receiver Operating Characteristic - M-ary Hypotheses Testing Estimation Theory : - Random parameter estimation : MS, MAP estimators - Nonrandom and unknown parameter estimation : ML estimator - Cramer-Rao lower bound - Composite Hypotheses - The general Gaussian problem Representation of Random Processes: - Orthogonal representation of signals - Random process characterization - White noise processes Detection of continuous signals - Detection of known signals in white Gaussian noise
References : Van Trees, Detection, Estimation, and Modulation Theory, Part I, Wiley, 2001.; Shanmugan and Breipohl, Random Signals, Wiley, 1988. ; H.V. Poor, An Introduction to Signal Detection and Estimation, Fall/ Springer, New York, 1994.; C.W. Helstrom, Elements of Signal Detection and Estimation, Prentice Hall, 1995.
Course Outline Weekly
Weeks Topics
1 Binary Hypothesis Testing
2 Optimum Decision Criteria
3 Decision Performance
4 M-ary Hypotheses Testing
5 Random parameter estimation
6 Nonrandom parameter estimation
7 Cramer-Rao inequality
8 Composite Hypotheses
9 The general Gaussian problem
10 Midterm Exam
11 Orthogonal representation of signals
12 Representation of Random Processes
13 White noise processes
14 Detection of known signals in white Gaussian noise
15 Preparation Week for Final Exams
16 Final exam
Assessment Methods
Course activities Number Percentage
Attendance 0 0
Laboratory 0 0
Application 0 0
Field activities 0 0
Specific practical training 0 0
Assignments 6 15
Presentation 0 0
Project 0 0
Seminar 0 0
Quiz 0 0
Midterms 1 40
Final exam 1 45
Total 100
Percentage of semester activities contributing grade success 55
Percentage of final exam contributing grade success 45
Total 100
Workload and ECTS Calculation
Course activities Number Duration (hours) Total workload
Course Duration 14 3 42
Laboratory 0 0 0
Application 0 0 0
Specific practical training 0 0 0
Field activities 0 0 0
Study Hours Out of Class (Preliminary work, reinforcement, etc.) 14 7 98
Presentation / Seminar Preparation 0 0 0
Project 0 0 0
Homework assignment 6 5 30
Quiz 0 0 0
Midterms (Study duration) 1 32 32
Final Exam (Study duration) 1 38 38
Total workload 36 85 240
Matrix Of The Course Learning Outcomes Versus Program Outcomes
Key learning outcomes Contribution level
1 2 3 4 5
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.
2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering.
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.
4. Designs and runs research projects, analyzes and interprets the results.
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects.
6. Produces novel solutions for problems.
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.
8. Follows technological developments, improves him/herself , easily adapts to new conditions.
9. Is aware of ethical, social and environmental impacts of his/her work.
10. Can present his/her ideas and works in written and oral form effectively; uses English effectively.
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest
General Information | Course & Exam Schedules | Real-time Course & Classroom Status
Undergraduate Curriculum | Open Courses, Sections and Supervisors | Weekly Course Schedule | Examination Schedules | Information for Registration | Prerequisite and Concurrent Courses | Legal Info and Documents for Internship | Academic Advisors for Undergraduate Program | Information for ELE 401-402 Graduation Project | Virtual Exhibitions of Graduation Projects | Program Educational Objectives & Student Outcomes | ECTS Course Catalog | HU Registrar's Office
Graduate Curriculum | Open Courses and Supervisors | Weekly Course Schedule | Final Examinations Schedule | Schedule of Graduate Thesis Defences and Seminars | Information for Registration | ECTS Course Catalog - Master's Degree | ECTS Course Catalog - PhD Degree | HU Graduate School of Science and Engineering