Unit name | Optimum Signal Processing |
---|---|
Unit code | EENGM1000 |
Credit points | 10 |
Level of study | M/7 |
Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
Unit director | Professor. Achim |
Open unit status | Not open |
Pre-requisites |
EENGM1400 |
Co-requisites |
None |
School/department | School of Engineering Mathematics and Technology |
Faculty | Faculty of Engineering |
The aim of this module is to bridge the gap between classical and modern methods of signal analysis, with an emphasis on the processing of stochastic signals. Students will gain an awareness of optimum signal processing methods primarily based on the least squares error criterion. Optimum filter design will be presented, based on the Wiener filter followed by LMS and RLS filter realizations. Three key application uses of this technology are spectrum estimation, noise cancellation and beamforming. These will be covered from a theoretical and application perspective. An introduction to advanced parameter estimation techniques will also be presented.
Elements:
On completing this unit, the student will be able to:
Lectures
Exam, 2 hours, 100% (All ILOs)
Hayes, M.H., Statistical Digital Signal Processing and Modeling, New York : Wiley, ISBN:0 4715 9431 8 (TK5102.9 HAY)
Kay, S., Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory, Prentice Hall, ISBN:0 1334 5711 7
Clarkson, Optimum and Adaptive Signal Processing, CRC Press, 1993, ISBN:0 8493 8609 8 (TK 5142.5 CLA)
Haykin, S., Modern Filters, Macmillan, 1989, ISBN:0 0235 2750 1 (TK 7872.FSHAY)