Entry requirements

120 credits in science/engineering including Signals and Systems as well as Probability and Statistics. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course the student shall be able to:

• design digital frequency selective filters according to practical limitations of a given problem,
• interpret and use the concepts of covariance matrices, auto-covariance, cross-covariance, wide-sense stationary processes and power density spectrum,
• design and implement optimal linear filters, such as Kalman and Wiener filters, and evaluate their applicability, optimality conditions and limitations for a given problem,
• design and implement parameter estimation methods, such as least-square (LS) solutions, and evaluate their limitations for a given problem,
• design and implement adaptive filters, with adaptation schemes such as LMS, RLS and evaluate their limitations for a given problem,
• implement the estimation methods introduced in the course using a numerical platform, such as MATLAB, and perform real-time or batch processing as appropriate.

Content

Digital frequency selective filters. Basic theory of stationary stochastic processes. Auto-covariance, cross-covariance. Power spectral density. Optimal linear estimation. Wiener filter. Kalman filter. Least mean square (LMS). Parameter estimation. Least-square estimation. Recursive least square (RLS).

Instruction

Lectures, guest lectures, assignments, tutorials and project supervision.

Assessment

Written exam at the end of the course (7 hp), oral and written presentation of assignments (1 hp), and oral and written presentation of project assignment (2 hp).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.