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Advanced Signal Processing (ELEN90052)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 1
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AIMS

This subject provides an in-depth introduction to statistical signal processing.

INDICATIVE CONTENT

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

Generic skills

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: 8 November 2024