EE4C03 Statistical digital signal processing

Topics: A second course on digital signal processing: random signals, covariances, linear prediction, Levinson and Schur algorithm, spectrum estimation, optimal filtering, Wiener and Kalman filters, LMS and RLS algorithm
This is a second course in discrete-time signal processing, with a focus on random signals. It provides a comprehensive treatment of signal processing algorithms for modeling discrete-time signals, designing optimum filters, estimation of the power spectrum of a random process, and implementing adaptive filters. These are important topics that are frequently encountered in professional engineering, and major applications such as digital communication, array processing, and multimedia (speech and audio processing, image processing). The course provides a framework that connects signal models to filter structures, formulates filter design as an optimization problem, solved in turn via linear algebra techniques applied to structured matrices. The connections between these topics are strong, and provide insights that can also be used in other disciplines.

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).

Teachers Geert Leus

Signal processing for communication and networking, with applications to underwater communication, cognitive radio and sensor networks. Alle-Jan van der Veen

Array signal processing; Signal processing for communications

Last modified: 2016-02-24


Credits: 5 EC
Period: 5/0/0/0


Geert Leus

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