Note: This is an archived Handbook entry from 2016.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2016:Semester 2, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: 36 hours of lectures |
Total Time Commitment:
|Recommended Background Knowledge:|| |
Knowledge of probability and random models equivalent to:
Study Period Commencement:
Knowledge of signals and systems concept, equivalent to:
Study Period Commencement:
Semester 2, Winter Term
|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 Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
CoordinatorProf Erik Weyer
The aim of this subject is to give students a rigorous introduction to the mathematical tools commonly employed in statistical signal processing.
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 (ILOs)
On completion of this subject the student is expected to:
1. Use the principle of orthogonality to derive least squares system identification and minimum mean square error state estimation algorithms
Intended Learning Outcomes (ILOs) 1-3 are assessed in the final written exam and through submitted homework assignments.
|Prescribed Texts:|| |
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
Doctor of Philosophy - Engineering |
Download PDF version.