[an error occurred while processing this directive]

Aim

This course will give thorough knowledge of optimal linear estimation theory. Kalman and Weiner filtering are systematic methods to solve many estimation problems in modern technical applications and the student will be able to apply these methods to estimation problems. The course assumes familiarity with basic concepts from matrix theory, linear algebra, and linear system theory. We will treat optimal linear estimation, which is encountered in many areas of engineering such as communications, control, and signal processing, and also in several other fields, e.g., econometrics and statistics. The course is directed towards the students who intend to work with development and research within these fields. 

Who should attend

This is a graduate level course that can be taken by undergraduate students who are admitted. Only students with adequate prerequisities will be admitted. It requires a large amount of self study. There are two versions of the course, a 6 ECTS credit course requiring only homework problems and a project assignment. The 12 ECTS credit version also requires an examination and the presentation of a special topic. 

Syllabus

Basic estimation theory, least squares problems.

The innovations process.

State-space models.

Wiener filtering.

Discrete-time Kalman filters.

Continous-time Kalman filters.

Properties of Kalman filters.

Smoothing.

Implementation aspects.