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Statistical Signal Processing (ELEN90079)
Graduate courseworkPoints: 12.5Not available in 2020
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
Overview
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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: 30 January 2024
Eligibility and requirements
Prerequisites
Admission into 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)
Semester 2 (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)
Semester 1 (On Campus - Parkville)
|
12.5 |
ELEN90058 | Signal Processing |
Semester 2 (On Campus - Parkville)
Semester 1 (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: 30 January 2024
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
Description | Timing | Percentage |
---|---|---|
Continuous assessment of assignments, not exceeding 60 pages in total over the semester. The continuous assessment consists of two projects to be submitted in Week 7 and Week 12 respectively
| From Week 7 to Week 12 | 20% |
Final examination
| End of semester | 80% |
Additional details
Intended Learning Outcomes (ILOs) 1-3 are assessed in the final written exam and through submitted homework assignments.
Last updated: 30 January 2024
Dates & times
Not available in 2020
Time commitment details
200 hours.
Last updated: 30 January 2024
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: 30 January 2024