EE4C03 Statistical digital signal processing
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 and solution approaches;
- Wiener and Kalman filtering;
- Spectrum estimation (nonparametric and parametric), frequency estimation (Pisarenko, MUSIC algorithm);
- Adaptive filtering (LMS, RLS).
dr.ir. Justin Dauwels
Machine learning, with applications to autonomous vehicles and biomedical signal processing
prof.dr.ir. Geert Leus
Signal processing for communications, with applications to underwater communications, cognitive radio, and multiple-input multiple-output (MIMO) systems. Signal processing for (compressive) sensing with applications to ultrasound imaging and radar. Distributed signal processing. Graph signal processing.
prof.dr.ir. Alle-Jan van der Veen
Array signal processing; Signal processing for communications
Last modified: 2021-08-26