MSc in Electrical Engineering

Course Information Package

Course Unit TitleDETECTION AND ESTIMATION THEORY
Course Unit CodeAEEE513
Course Unit DetailsMSc Electrical Engineering (Technical Electives) -
Number of ECTS credits allocated7
Learning Outcomes of the course unitBy the end of the course, the students should be able to:
  1. Describe random variables, moments, expectations and correlations. Build confidence in the characterization and use of distributions.
  2. Evaluate random processes and discrete time random processes. Classification and use of AR, MA and ARMA processes.
  3. Develop abilities in applying Baye’s criterion, binary and M-ary hypothesis testing.
  4. Describe optimum filters. Analyze and develop Wiener and Kalman filters.
  5. Demonstrate knowledge in detection parameter estimation methods, the concept of the correlation receiver, matched filter and ML and MAP estimations.
Mode of DeliveryFace-to-face
PrerequisitesNONECo-requisitesNONE
Recommended optional program componentsNONE
Course Contents

Random processes.

Expectations. Correlation functions. Stochastic signals and LTI systems. Ergodicity.

Discrete-time random processes.

Eigenvalues and eigenvectors. AR, MA and ARMA random processes. Markov chains.

Decision theory.

Baye’s criterion. Binary hypothesis testing. M-ary Hypothesis Testing. Minimax Criterion.

Parameter estimation.

Maximum likelihood estimation. Baye’s estimation. Least square estimation. Cramer-Rao inequality. Best linear unbiased estimator.

Filtering.

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:
Textbooks
  • Mourad Barkat, “Signal Detection And Estimation”, Artech House Radar Library, 2005.
References
  • B.C. Levy, Principles of Signal Detection and Parameter Estimation. New York: Springer-Verlag, 2008.
  • Ab. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin, Bayesian Data Analysis. 2nd ed. New York: Chapman & Hall, 2004.
  • L. Wasserman, All of Statistics: A Concise Course in Statistical Inference. New York: Springer-Verlag, 2004.
  • S.M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory. Englewood Cliffs, NJ: Prentice-Hall, 1998.
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
Assignments20%
Tests30%
Final Exam50%
Language of instructionEnglish
Work placement(s)NO