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