| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| EEE6634 | Space and Time Signal Processing | Fall | 3 | 0 | 3 | 12 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi ZAFER İŞCAN |
| Course Objectives: | The course targets presentation of an an overview of space time processing techniques, with particular reference to array processing. Upon completion of the course, the student will have an understanding of array processing techniques, experience with analytical, and numerical tools and will be able to apply various algorithms and be able to specify the most appropriate one for the application. |
|
The students who have succeeded in this course; I. Learn principles of parametric and non-parametric array processing algorithms. II. Learn performance analysis of a variety of algorithms. III. Read, understand and interpret the literature related to space time processing. IV. Understand and appreciate practical issues in space time processing system design. |
| 1. Spectral Estimation Basics. 2. Intro to Non-parametric Array Processing 3. Beamsteering, Conventional Beamformer 4. Optimal Beamformer 5. Adaptive Beamformer 6. Intro to Parametric Array Processing 7. Eigen Based Methods (MUSIC, ESPRİT) 8. Non Linear Least Squared and Maximum Likelihood Est. 9. Active Processing 10. Likelihood Ratio Tests 11. Passive Processing 12. Cyclostationary Processing 13. Detection, Range Estimation 14. Application. |
| Week | Subject | Related Preparation | |
| 1) | Spectral Estimation Basics | ||
| 2) | Intro to Non-parametric Array Processing | ||
| 3) | Beamsteering, Conventional Beamformer | ||
| 4) | Optimal Beamformer | ||
| 5) | Adaptive Beamformer | ||
| 6) | Intro to Parametric Array Processing | ||
| 7) | Eigen Based Methods (MUSIC, ESPRIT) | ||
| 8) | Non Linear Least Squared and Maximum Likelihood Est. | ||
| 9) | Midterm Exam | ||
| 10) | Active Processing | ||
| 11) | Likelihood Ratio Tests | ||
| 12) | Passive Processing | ||
| 13) | Cyclostationary Processing | ||
| 14) | Detection, Range Estimation | ||
| 15) | Preperation for Final | ||
| 16) | Final | ||
| Course Notes: | Modern Spectral Estimation Theory and Application, Steven Kay, Prentice Hall. ISBN -13-598582-X |
| References: |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | 3 | % 10 |
| Homework Assignments | 4 | % 10 |
| Presentation | % 0 | |
| Project | 3 | % 10 |
| Seminar | % 0 | |
| Midterms | 2 | % 30 |
| Preliminary Jury | % 0 | |
| Final | 2 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload | |
| Course Hours | 14 | 42 | |
| Laboratory | |||
| Application | |||
| Special Course Internship (Work Placement) | |||
| Field Work | |||
| Study Hours Out of Class | |||
| Presentations / Seminar | |||
| Project | 5 | 40 | |
| Homework Assignments | |||
| Quizzes | |||
| Preliminary Jury | |||
| Midterms | 4 | 20 | |
| Paper Submission | |||
| Jury | |||
| Final | 4 | 40 | |
| Total Workload | 142 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |