| 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 |
: |
P. Moulin and V. Veeravalli. Statistical Inference for Engineers and Data Scientists. Cambridge: Cambridge University Press. 2018.; 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, 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 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| Key learning outcomes |
Contribution level |
| 1 |
2 |
3 |
4 |
5 |
| 1. |
Has highest level of knowledge in certain areas of Electrical and Electronics Engineering. | | | | | |
| 2. |
Has knowledge, skills and and competence to develop novel approaches in science and technology. | | | | | |
| 3. |
Follows the scientific literature, and the developments in his/her field, critically analyze, synthesize, interpret and apply them effectively in his/her research. | | | | | |
| 4. |
Can independently carry out all stages of a novel research project. | | | | | |
| 5. |
Designs, plans and manages novel research projects; can lead multidisiplinary projects. | | | | | |
| 6. |
Contributes to the science and technology literature. | | | | | |
| 7. |
Can present his/her ideas and works in written and oral forms effectively; in Turkish or English. | | | | | |
| 8. |
Is aware of his/her social responsibilities, evaluates scientific and technological developments with impartiality and ethical responsibility and disseminates them. | | | | | |
