Course content

The course emphasizes general estimation theory with applications ons various signal processing problems.

Learning outcome

A. Knowledge:
1) A basic understanding of classical and Bayesian estimation theory and applications of these in signal processing.
2) An understanding of properties of minimum variance unbiased estimators.
3) An understanding of properties of the maximum likelihood estimator and its use in signal processing.
4) Least Squares and its use in signal processing

B. Skills:
1) Ability to assess the quality of an estimator by its variance and Cramer Rao Lower Bound
2) Ability to 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