Note: This is an archived Handbook entry from 2011.
|Dates & Locations:|| |
This subject is not offered in 2011.
|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 |
|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/|
|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.|
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
Master of Engineering Science (Electrical and Electronic) |
Master of Philosophy - Engineering
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