Advanced Signal Processing

This subject has the following teaching availabilities in 2012:

Prerequisites for this subject are:

ELEN90058 Signal Processing ( prior to 2011, ELEN30008 Signal Processing 1)

AND

ELEN90054 Probability and Random Models( prior to 2011, ELEN30002 Stochastic Signals and Systems )

• Applications of statistical signal processing;
• A review of stochastic signals and systems fundamentals – random processes, white noise, stationarity, auto- and cross-correlation functions, spectral- and cross-spectral densities, properties of linear time-invariant systems excited by white noise;
• Parameter estimation - least squares and its properties, recursive least squares and least mean squares, optimisation-based methods, maximum likelihood methods;
• Kalman filtering and selected topics from spectral estimation, Wiener and Markov filtering.

• Apply fundamental mathematical tools, in particular stochastic techniques, in the analysis and design of signal processing systems;
• Recognise estimation problems and design, implement and analyses algorithms for solving them;
• Use software packages such as MATLAB for the analysis and design of signal processing systems;
• Implement signal processing systems with DSP based development platforms.

• One written examination, not exceeding three hours at the end of semester, worth 70% (must pass written exam to pass subject);
• Continuous assessment of submitted project work, not exceeding 30 pages over the semester, worth 30%.

This subject is not available as a breadth subject.

• Ability to apply knowledge of basic science and engineering fundamentals
• In-depth technical competence in at least one engineering discipline
• Ability to undertake problem identification, formulation and solution
• Ability to utilise a systems approach to design and operational performance
• Capacity for independent critical thought, rational inquiry and self-directed learning
• Ability to communicate effectively, with the engineering team and with the community at large