Course - Signal and Estimation Theory - TT8111
Signal and Estimation Theory
About
About the course
Course content
This course is taught every second year (on even years) if enough students. The course emphasizes general estimation theory with applications on various signal processing problems.
Learning outcome
A. Knowledge:
Student has a
1) detailed knowledge of classical and Bayesian estimation theory and applications of these in signal processing.
2) a detailed understanding of properties of minimum variance unbiased estimators.
3) a detailed understanding of the properties of the maximum likelihood estimator and its use in signal processing.
4) detailed knowledge of least squares and its use in signal processing
B. Skills:
Students can
1) assess the quality of an estimator by its variance and Cramer Rao Lower Bound
2) consider different estimators and choose an appropriate method for a given signal processing purpose.
Learning methods and activities
Lectures, colloquiums, PC assignments and exercises.
Compulsory assignments
- Exercises
Recommended previous knowledge
Fundamental knowledge in signal processing, statistics and probability theory.
Required previous knowledge
Fundamental knowledge in signal processing, statistics and probability theory.
Course materials
S. M. Kay: Fundamentals of Statistical Signal Processing, Vol. 1, Prentice Hall, 1993.
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| TT8309 | 5 sp | Autumn 2011 |
| TTT4240 | 7.5 sp | Autumn 2011 |
Subject areas
- Electronics
- Telecommunication
- Technological subjects
Contact information
Course coordinator
Lecturers
Department with academic responsibility
Examination
Examination
Ordinary examination - Spring 2026
Oral exam
To 2026-05-21 Time 09:00 Duration 1 hours