ACADEMICS
Course Details

ELE770 - Statistical Signal Processing

2022-2023 Fall term information
The course is not open this term
ELE770 - Statistical Signal Processing
Program Theoretýcal hours Practical hours Local credit ECTS credit
PhD 3 0 3 10
Obligation : Elective
Prerequisite courses : -
Concurrent courses : -
Delivery modes : Face-to-Face
Learning and teaching strategies : Lecture, Question and Answer, Problem Solving
Course objective : Successful students are expected to gain the following abilities: Knowledge of basic estimation, filtering, prediction methods such as Bayes, MAP, MLE, LMSE, Wiener, Levinson and Kalman filters.
Learning outcomes : A student completing the course successfully Recognizes statistical signal processing problems, Models the encountered problems in suitable forms Knows which algorithms can be used to solve the problem established, knows advantages and disadvantages of these algorithms, Applies the techniques and algorithms learnt in the class in projects, Has the adequate knowledge to follow and understand advanced up-to-date algorithms.
Course content : Metric space, inner product, norm etc. definitions. Review of Probability and stochastic processes. Estimation methods: Bayes, MAP, MLE, LMSE. Filtering, estimation and prediction methods: Wiener, Levinson ve Kalman filters.
References : 1-T. Moon and W. Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice-Hall.; 2-S.J. Orfanidis, Optimum Signal Processing, McGraww Hill.; 3-S. Kay, Fundamentals of Statistical Signal Processing, Vol.I-II, Prentice Hall.; 4-Lecture Notes.
Course Outline Weekly
Weeks Topics
1 Metric Spaces.
2 Norms, Orthogonal Spaces, Projections, Random Vectors.
3 Orthogonal Projections, Gram-Schmidt Orthogonalization.
4 Random Processes, Gaussian Processes, Markov Processes.
5 Random State Models.
6 Analysis of Systems, Spectral Factorization, Rational Modeling.
7 Bayesian Estimation, MAP, MLE,MSE.
8 LMSE.
9 Term Exam.
10 Wiener Filter.
11 Wiener Filter.
12 Levinson Filter.
13 Kalman Filter.
14 Kalman Filter.
15 Final Exam.
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 8 15
Presentation 0 0
Project 0 0
Seminar 0 0
Quiz 0 0
Midterms 1 35
Final exam 1 50
Total 100
Percentage of semester activities contributing grade success 50
Percentage of final exam contributing grade success 50
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 9 126
Presentation / Seminar Preparation 0 0 0
Project 0 0 0
Homework assignment 8 8 64
Quiz 0 0 0
Midterms (Study duration) 1 30 30
Final Exam (Study duration) 1 40 40
Total workload 38 90 302
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.
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest
General Information | Course & Exam Schedules | Real-time Course & Classroom Status
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