Statistical Signal Theory

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

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.

Lectures, colloquiums, PC assignments and exercises. If there is a re-sit examination, the examination form may be changed from written to oral.