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, the Levinson algorithm, the Schur algorithm;
- Wiener and Kalman filtering;
- Spectrum estimation (nonparametric and parametric), frequency estimation (Pisarenko, MUSIC algorithm);
- Adaptive filtering (LMS, RLS).
prof.dr.ir. Geert Leus
Signal processing for communication and networking, with applications to underwater communication, cognitive radio and sensor networks.
prof.dr.ir. Alle-Jan van der Veen
Array signal processing; Signal processing for communications
dr. Borbála Hunyadi
Biomedical signal processing
Last modified: 2019-09-12