ELE636 - DETECTION and ESTIMATION THEORY
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| DETECTION and ESTIMATION THEORY | ELE636 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prequisites | None. Students are expected to have taken ELE 324, ELE 425 courses. | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Problem Solving | |||||
| Instructor (s) | Department Faculty | |||||
| 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 |
| |||||
| 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 |
|---|---|
| Week 1 | Binary Hypothesis Testing |
| Week 2 | Optimum Decision Criteria |
| Week 3 | Decision Performance |
| Week 4 | M-ary Hypotheses Testing |
| Week 5 | Random parameter estimation |
| Week 6 | Nonrandom parameter estimation |
| Week 7 | Cramer-Rao inequality |
| Week 8 | Composite Hypotheses |
| Week 9 | The general Gaussian problem |
| Week 10 | Midterm Exam |
| Week 11 | Orthogonal representation of signals |
| Week 12 | Representation of Random Processes |
| Week 13 | White noise processes |
| Week 14 | Detection of known signals in white Gaussian noise |
| Week 15 | Preparation Week for Final Exams |
| Week 16 | Final exam |
Assesment 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 |
| Midterms | 1 | 40 |
| Final exam | 1 | 45 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 0 | 55 |
| Percentage of final exam contributing grade succes | 0 | 45 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 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, ect) | 14 | 7 | 98 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 6 | 5 | 30 |
| 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
| D.9. Key Learning Outcomes | Contrubition 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. | X | ||||
| 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. | X | ||||
| 3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems. | X | ||||
| 4. Designs and runs research projects, analyzes and interprets the results. | X | ||||
| 5. Designs, plans, and manages high level research projects; leads multidiciplinary projects. | X | ||||
| 6. Produces novel solutions for problems. | X | ||||
| 7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects. | X | ||||
| 8. Follows technological developments, improves him/herself , easily adapts to new conditions. | X | ||||
| 9. Is aware of ethical, social and environmental impacts of his/her work. | X | ||||
| 10. Can present his/her ideas and works in written and oral form effectively; uses English effectively | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest