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Semester 1 - Dual-Delivery
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This subject provides an in-depth introduction to statistical signal processing.
Students will study a selection of the following topics:
- 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, Wiener and Markov filtering;
- Power spectrum estimation.
This material will be complemented with the use of software tools (e.g. MATLAB) for computation and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory.
Intended learning outcomes
On completing this subject the student should be able to:
- Apply fundamental mathematical tools, in particular stochastic techniques, in the analysis and design of signal processing systems
- Recognise estimation problems and design, implement and analyse 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
On completing this subject, students will have developed the following skills:
- 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.
Last updated: 4 September 2021