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Statistical Signal Processing (ELEN90079)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Email: ewey@unimelb.edu.au
Overview
Availability | Semester 2 |
---|---|
Fees | Look up fees |
Topics include: State estimation algorithms (Kalman and Wiener filtering); parameter estimation algorithms (Least Squares, Maximum Likelihood, Maximum a Posteriori) and their adaptive versions.
Other topics to be selected from: system identification, spectral analysis, nonlinear filtering; hidden Markov model signal processing; expectation maximization algorithm; distributed detection and estimation; information-theoretic aspects of estimation and detection (Cramer Rao bound, Divergence measures).
Intended learning outcomes
Intended Learning Outcomes (ILOs)
The aim of this subject is to give students a rigorous introduction to the mathematical tools commonly employed in statistical signal processing.
Having completed this subject it is expected that the student be able to:
1. Use the principle of orthogonality to derive least squares system identification and minimum mean square error state estimation algorithms
2. Use probability theory to analyse properties of system identification and filtering algorithms
3. Formulate and solve optimal system identification and filtering problems
Generic 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;
- Expectation of the need to undertake lifelong learning, capacity to do so;
- Capacity for independent critical thought, rational inquiry and self-directed learning;
- Intellectual curiosity and creativity, including understanding of the philosophical and methodological bases of research activity;
- Openness to new ideas and unconventional critiques of received wisdom;
- Profound respect for truth and intellectual integrity, and for the ethics of scholarship.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Enrolment in a research higher degree (MPhil or PhD) in Engineering or approval from the subject coordinator.
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Knowledge of probability and random models equivalent to:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
ELEN90054 | Probability and Random Models | Semester 1 (On Campus - Parkville) |
12.5 |
Knowledge of signals and systems concept, equivalent to:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
ELEN30012 | Signals and Systems |
Semester 2 (On Campus - Parkville)
Winter Term (On Campus - Parkville)
|
12.5 |
ELEN90058 | Signal Processing | Semester 2 (On Campus - Parkville) |
12.5 |
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Additional details
- Continuous assessment of assignments, not exceeding 60 pages in total over the semester, requiring approximately 25-30 hours of work in total. The continuous assessment consists of two projects to be submitted in Week 7 and Week 12 respectively (20%);
- Final 3-hour examination at the end of semester (80%).
Hurdle Requirement: Students must pass the final exam in order to pass the subject.
Intended Learning Outcomes (ILOs) 1-3 are assessed in the final written exam and through submitted homework assignments.
Last updated: 3 November 2022
Dates & times
- Semester 2
Principal coordinator Erik Weyer Mode of delivery On Campus (Parkville) Contact hours 36 hours of lectures Total time commitment 200 hours Teaching period 24 July 2017 to 22 October 2017 Last self-enrol date 4 August 2017 Census date 31 August 2017 Last date to withdraw without fail 22 September 2017 Assessment period ends 17 November 2017 Semester 2 contact information
Email: ewey@unimelb.edu.au
Time commitment details
200 hours.
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required
Last updated: 3 November 2022