Statistical Signal Processing
Subject 431-688 (2008)
Note: This is an archived Handbook entry from 2008.Search for this in the current handbook
| Credit Points: | 12.500 | ||||||||||||
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| Level: | Graduate/Postgraduate | ||||||||||||
| Dates & Locations: | This subject has the following teaching availabilities in 2008: Semester 2, - Taught on campus.
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: Not available | ||||||||||||
| Prerequisites: | None | ||||||||||||
| 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 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 |
Coordinator
Brian Krongold| 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. |
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| Assessment: | Continuous assessment (40%) to the equivalent of 3 hours writing time. Final Exam 3 hours, worth 60%. Students are required to pass the final examination in order to pass the subject as a whole. |
| Prescribed Texts: | None |
| Breadth Options: | This subject is not available as a breadth subject. |
| Fees Information: | Subject EFTSL, Level, Discipline & Census Date |
| Generic Skills: | The aim of this subject is to give students a rigorous introduction to the mathematical tools commonly employed in statistical signal processing. |
| Related Course(s): |
Ph.D.- Engineering |
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