Statistical Signal Processing

Subject ELEN90029 (2010)

Note: This is an archived Handbook entry from 2010.

Credit Points: 12.50
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2010:

Semester 2, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable


Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 24 hours; Non-contact time commitment: 96 hours
Total Time Commitment: 120 hours
Prerequisites: Enrolment in a research higher degree(Masters or PhD) in Engineering
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements: For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website: http://www.services.unimelb.edu.au/disability/

Coordinator

Dr Marcus Brazil

Contact

Melbourne School of Engineering Office
Building 173, Grattan Street
The University of Melbourne
VIC 3010 Australia
General telephone enquiries
+ 61 3 8344 6703
+ 61 3 8344 6507
Facsimiles
+ 61 3 9349 2182
+ 61 3 8344 7707
Email
eng-info@unimelb.edu.au
Subject Overview: Topics include: a review of stochastic processes detection of random signals in noise; dynamical system models and associated state estimation algorithms (Kalman and Wiener filtering); parameter estimation algorithms (Least Squares, Maximum Likelihood) and their adaptive versions. Other topics to be selected from: 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); array signal processing.
Objectives: The aim of this subject is to give students a rigorous introduction to the mathematical tools commonly employed in statistical signal processing.
Assessment:
  • Continuous assessment of homework assignments, not exceeding 30 pages in total over the semester, worth 40%;
  • Final examination at the end of semester, worth 60%. Students must pass the final exam in order to pass the subject.
Prescribed Texts: None
Breadth Options:

This subject is not available as a breadth subject.
Fees Information: Subject EFTSL, Level, Discipline & Census Date
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
Related Course(s): Master of Applied Science (Electrical and Electronic)
Master of Engineering Science (Electrical and Electronic)
Master of Philosophy - Engineering
Ph.D.- Engineering

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