Syllabus 2008/2009 ETT042

Syllabus academic year 2008/2009
(Created 2008-07-17.)

ADAPTIVE SIGNAL PROCESSING

ETT042

Higher education credits: 6. Grading scale: TH. Level: A (Second level). Language of instruction: The course might be given in English. Optional for: C4, C4sst, D4, D4sst, E4, E4bg, E4ss, F4, MWIR2, Pi4, Pi4sbs. Course coordinator: Universitetslektor Martin Stridh, martin.stridh@eit.lth.se, Inst för elektro- och informationsteknik. Prerequisites: ESS040 Digital Signal Processing or ETI265 Signal Processing in Multimedia or ETI080 Signals and Communications. Assessment: The grade is mainly based on the exam in the end of the course. The grade can be affected upwards (0.5 points) with volontary home assigments during the course. The opportunity to make the home assignment is only available during the course and the result is valid during one year. Further information: Exercises 14 h, computer exercises 14 h and laboratory work 2 x 4 h. The course might be given in English. Home page: http://www.eit.lth.se/course/ett042.

Aim
The course presents signal processing methodology and solutions to problems where digital systems tune in automatically and adapt to the environment. The student is given enough theoretical and practical knowledge to independently be able to formulate the mathematical problem, solve it and implement the solution for use with real-life signals.

Knowledge and understanding
For a passing grade the student must

have knowledge about and understand the main concepts in adaptive filter theory
be able to apply the most commonly used methods to real problems and real-life signals (Matlab-level)
be able to formulate mathematical problems based on described situations

Skills and abilities
For a passing grade the student must

be able to explain the main principles behind the most common methods (LMS and RLS)
be able to explain/calculate the convergence and stability properties for these methods
be able to sketch the most common block diagrams/structures used for adaptive filters and their properties
be able to set parameters needed to make the algorithms work
be able to foresee the consequences for the algorithms when implemented in fixed-point arithmetic
be able to implement adaptive filters

Judgement and approach
For a passing grade the student must

have the ability to analyze, evaluate and implement adaptive algorithms, and be able to interpret and describe the principles which they are based on.
have the insight that many different technical problems can be solved using the same methods.

Contents
Basics about adaptive filters

From optimal to adaptive filters
Cost functions, minimization problems and iterative procedures
Convergence and tracking capability, implementation aspects
Strategies for how to connect adaptive filters

The LMS family

Principle and derivation
Convergence analysis and parameter settings
Variants including Normalized LMS, Leaky LMS, Fast LMS and Sign LMS
Matlab implementation
LMS in fixed-point arithmetic
Principle and derivation
Parameter settings

The RLS family

Aspects when used
Matlab implementation
Numerical properties

Literature
Haykin S: Adaptive Filter Theory, Fourth Edition, Prentice-Hall 2001. Hardcover: ISBN 0-13-090126-1.