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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
INTENDED LEARNING OUTCOMES (ILOs)
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: 18 December 2020