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.