Syllabus academic year 2011/2012
(Created 2011-09-01.)
ADAPTIVE SIGNAL PROCESSING | ETT042 |
Credits: 6.
Grading scale: TH.
Cycle: A
(Second Cycle).
Main field: Technology.
Language of instruction: The course might be given in English.
Optional for: C4, C4ssr, D4, D4ssr, E4, E4bg, E4ssr, F5, F5ssr, MWIR2, Pi4, Pi4ssr.
Course coordinator: Associate Professor Martin Stridh, martin.stridh@eit.lth.se, Electrical and Information Technology.
Recommended prerequisits: ESS040 Digital Signal Processing or ETI265 Signal Processing in Multimedia or EITF15 Signal processing - theory and applications.
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.