Statistical Digital Signal Processing and Modelling

The course treats: background in DSP, linear algebra and random processes; linear prediction, parametric methods such as Pade approximation, Prony's method and ARMA models; the Yule-Walker equations; Wiener and Kalman filtering; spectrum estimation (nonparametric and parametric), frequency estimation (Pisarenko, MUSIC algorithm); adaptive filtering (LMS, RLS). Part of the course is a track-specific take-home matlab assignment which addresses a practical modeling/estimation/tracking problem in power engineering, radar, speech processing, or bio signal processing.

Written tests (including in-class quizzes), which count for 80% of the final grade. The track-specific matlab assignment results in a report which is pass/fail (need to pass), furthermore it is graded and counts for 20% of the final grade.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations.

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).