Course Information Package
|Course Unit Title||DETECTION AND ESTIMATION THEORY|
|Course Unit Code||AEEE513|
|Course Unit Details||MSc Electrical Engineering (Technical Electives) -|
|Number of ECTS credits allocated||7|
|Learning Outcomes of the course unit||By the end of the course, the students should be able to:|
|Mode of Delivery||Face-to-face|
|Recommended optional program components||NONE|
Expectations. Correlation functions. Stochastic signals and LTI systems. Ergodicity.
Discrete-time random processes.
Eigenvalues and eigenvectors. AR, MA and ARMA random processes. Markov chains.
Baye’s criterion. Binary hypothesis testing. M-ary Hypothesis Testing. Minimax Criterion.
Maximum likelihood estimation. Baye’s estimation. Least square estimation. Cramer-Rao inequality. Best linear unbiased estimator.
Optimum unrealizable and realizable filters. Discrete Wiener filters. Kalman filter and prediction.
Detection and Parameter Estimation.Binary detection. Simple binary and general binary detection. M-ary detection. Correlation receiver. Matched filter receiver. ML estimation. MAP estimation. Detection
|Recommended and/or required reading:|
|Planned learning activities and teaching methods|
The teaching of this course is based on lectures (3 hours per week) in a classroom, using a combination of traditional teaching with notes on a white board, and where needed, slide presentations using a projector.
Examples regarding the material presented during the lectures are discussed and solved. Further questions related to particular topic issues are compiled by the students and answered, during the lecture or assigned as homework. Due to the level and type of the course the students are urged to participate in discussing the various topics and provide their opinion. Topic notes are compiled by students, during the lecture which serve to cover the main issues under consideration. Students are also required to heavily use the textbook assigned to the course in addition to other sources found in the library and elsewhere to broaden their perspective on the various issues presented in class and in the textbook.
Homework problems are assigned from the textbook and elsewhere as a turn in assignment or for homework practice. Also, students are advised to use the reference books for further reading and practice in solving related exercises. Tutorial problems are also submitted as homework and these are solved during lectures or the solutions are posted on the class webpage.
Students are assessed continuously and their knowledge is checked through tests with their assessment weight, date and time being set at the beginning of the semester via the course outline. They are prepared for final exam, by revision on the matter taught, problem solving and concept testing and are also trained to be able to deal with time constraints and revision timetable. The final assessment of the students is formative and summative and is assured to comply with the subject’s expected learning outcomes and the quality of the course.
|Assessment methods and criteria|
|Language of instruction||English|