BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
Course Introduction and Application Information
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| EEE6634 | Space and Time Signal Processing | Fall Spring |
3 | 0 | 3 | 12 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Basic information
| Language of instruction: | English |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi ZAFER İŞCAN |
| Recommended Optional Program Components: | None |
| 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. |
Learning Outcomes
|
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. |
Course Content
| 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. |
Weekly Detailed Course Contents
| 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 |
Sources
| Course Notes / Textbooks: | Modern Spectral Estimation Theory and Application, Steven Kay, Prentice Hall. ISBN -13-598582-X |
| References: |
Evaluation System
| Semester Requirements | Number of Activities | Level of Contribution |
| Quizzes | 3 | % 10 |
| Homework Assignments | 4 | % 10 |
| Project | 3 | % 10 |
| Midterms | 2 | % 30 |
| Final | 2 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| Total | % 100 | |
ECTS / Workload Table
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Project | 5 | 40 |
| Midterms | 4 | 20 |
| Final | 4 | 40 |
| Total Workload | 142 | |
Contribution of Learning Outcomes to Programme Outcomes
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |