Advanced signal processing

What it covers

The unit introduces the fundamentals of statistical signal processing with emphasis on stochastic models, estimation theory, parametric and non-parametric modelling and least squares methods. After a review of basic probability and random processes, the use of stochastic models for real world signals is illustrated. A family of algorithms for the creation, efficient representation and effective modelling is presented. Specifically, linear stochastic models are presented and the importance of correlation structure in deriving the parameters of such models is illustrated. The unit also covers how parametric and non-parametric models as well as statistical techniques are used to extract information from data signals corrupted by noise. The concept of estimation from real world data is presented, as opposed to the basic analysis of signals, transfer functions and power spectra. In particular, the fundamentals of linear estimation theory and optimal filtering to design advanced signal processing algorithms are presented.

Assessment

When and where