Adaptive Signal Processing (P01759)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : EEL-P-PGASP

This course will deal with the theory and applications of advanced signal processing topics, in particular modern spectral analysis, spectral estimation techniques and signal detection and the theory of adaptive filters and their applications.
Algorithms and structures for optimal linear filters: Optimum IIR filters, Inverse filtering and deconvolution, channel equalisation, matched and eigen-filters, Kalman filter; Adaptive Filters: Principles, Methods of Steepest descent, Recursive Least Squares based methods, Fast RLS methods, Performance Measures; Applications of Adaptive Filters: Adaptive Equalisation, Adaptive Noise Cancelling, Prediction; Modern Spectral Analysis: AR Spectral analysis, LPC, MV spectral analysis; Array Fundamentals: Beamforming, Optimum Array processing, Adaptive Beamforming

? Keywords : spectral analysis, spectral estimation, signal detection, adaptive filters, least squares methods

Entry Requirements

? Pre-requisites : Prior attendance of Statistical Signal Processing and Discrete-Time Signal Analysis is recommended.

? Costs : Compulsory book purchase: B. Mulgrew, P.M. Grant, and J.S. Thompson, Digital Signal Processing: Concepts and Applications (2nd Ed), Palgrave, 2003

Subject Areas

Home subject area

Postgraduate (School of Engineering and Electronics), (School of Engineering and Electronics, Schedule M)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 3 hour(s) per week for 11 weeks

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	11:10	13:00	KB

Summary of Intended Learning Outcomes

The students should understand the foundations of modern signal processing methods such as spectral analysis and estimation as well as adaptive filters. They should also be able to understand practical applications of these concepts and their limitations.