Optimal Estimation Theory
Computer Engineering (MSc.)
Institute of Natural and Applied Sciences
Second Cycle (Master's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Optimal Estimation Theory | BLM509 | Elective | Master's degree | 1 | Fall | 8 |
Name of Lecturer(s)
Prof. Dr. Yaşar BECERİKLİ
Associate Prof. Dr. Orhan AKBULUT
Learning Outcomes of the Course Unit
1) Comment on optimal estimation theory
2) Implement the estimation of the discrete and continous sytems
3) Design the Wiener filter and comprehend the optimal state estimation
4) Implement the Kalman filter and its application
5) Design Advanced subjects in estimation theory such as extended Kalman Filter
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | ||
| Learning Outcomes | ||||||||||||
| 1 | No relation | No relation | High | No relation | No relation | Middle | Middle | No relation | No relation | No relation | No relation | |
| 2 | No relation | No relation | High | No relation | No relation | Middle | Middle | No relation | No relation | No relation | No relation | |
| 3 | No relation | No relation | High | No relation | No relation | Middle | Middle | No relation | No relation | No relation | No relation | |
| 4 | No relation | No relation | High | No relation | No relation | Middle | Middle | No relation | No relation | No relation | No relation | |
| 5 | No relation | No relation | High | No relation | No relation | Middle | Middle | No relation | No relation | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
System Identification Theory
Course Contents
This course equips candidates with in-depth knowledge on introduction to estimation theory, mathmetical operations, matrix differential calculetion, least squares calculation, successive estimation, random variables, distribution functions, static processes, Wiener filter problem, spektral factorisation, random access systems,optimum state estimation problem, Kalman filter and stability, continual condition state, linear discrete time random systems, Kalman filter types and extended Kalman filter
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
40% |
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Contribution of Final Examination to Course Grade |
60% |
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Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required