Syllabus academic year 2007/2008

Higher education credits: 6. Grading scale: TH. Level: A (Second level). Language of instruction: The course will be given in Swedish. Optional for: C4, C4sst, D4, D4sst, E4bg, E4mt, E4ss, F4, F4rs, MWIR2, Pi4sbs. Course coordinator: Universitetslektor Bengt Mandersson,, Elektrovetenskap. Prerequisites: ESS040 Digital Signal Processing or ETI265 Signal Processing in Multimedia or ETT080 Signals and Communications. Assessment: Written exam, fulfilled laboratory work and partial tests during the course. Home page:

The course gives the basic knowledge in statistical signal processing and treats the theory and applications of optimal methods in filter design. The filter design methods are based on the statistical properties of the information signals as well as the additive noise.

Knowledge and understanding
For a passing grade the student must

Skills and abilities
For a passing grade the student must

Judgement and approach
For a passing grade the student must

The following items are treated in the course: Matrices, random processes, spectral factorization. Signal modelling (IIR/FIR) using the Prony’ method. Normal equation and Levinson-Durbin recursive method, lattice filters. Estimation of reflection coefficients using Burgs algorithm. Optimal IIR and FIR filters (‘Wiener filters’), linear prediction, noise cancellation. Spectral estimation using non-parametric methods. Eigenvector methods in estimation of sinusoids in noise (Pisarenko, Music).

Applications: Filter for optimal noise cancellations are used in a wide area, i.e. mobile communication, acoustical signal processing, biomedical signals (EEG, ECG ). Spectral estimation has also a wide area of applications. Fast algorithms in signal processing are of great importance for VLSI design.

The literature mentioned was used last year. Hayes M: Statistical Digital Signal Processing and Modelling. John Wiley & Sons 1996. ISBN: 0471594318.