Course syllabus
Optimal och adaptiv signalbehandling
Optimum and Adaptive Signal Processing
EITN60, 7,5 credits, A (Second Cycle)
Valid for: 2016/17
Decided by: Education Board A
Date of Decision: 2016-04-05
General Information
Elective for: BME4-sbh, C4-ssr, D4-ssr, E4-ss, E4-bg, F5, F5-r, F5-ss, MWIR2, Pi4-ssr
Language of instruction: The course will be given in English
Aim
The course provides basic knowledge in statistical signal
processing and the theory of optimal methods and how they can be
applied. 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.
Learning outcomes
Knowledge and understanding
For a passing grade the student must
- have knowledge about and understand the main concepts in
optimum and 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
Competences and skills
For a passing grade the student must
- be able to explain the main principles behind the most common
adaptive 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
Optimum filtering
- Wiener filters
- Linear prediciton
- The Levinson-Durbin algorithm
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
Examination details
Grading scale: TH
Assessment: The grade is based on the exam in the end of the course.
Parts
Code: 0114. Name: Adaptive Signal Processing.
Credits: 6. Grading scale: TH. Assessment: Written Examination.
Code: 0214. Name: Project.
Credits: 1,5. Grading scale: UG. Assessment: Project Report.
Admission
Required prior knowledge: ESS040 Digital Signal Processing or ETI265 Signal Processing in Multimedia or EITF15 Signal processing - theory and applications.
The number of participants is limited to: No
The course overlaps following course/s: ETTN05, ETT042
Reading list
- Haykin S: Adaptive Filter Theory, Fifth Edition. Pearson, 2014, ISBN: 0-273-76408-X.
Contact and other information
Course coordinator: Frida Sandberg, frida.sandberg@bme.lth.se
Course homepage: http://www.bme.lth.se/course-pages/optimal-och-adaptiv-signalbehandling/optimum-and-adaptive-signal-processing/
Further information: Exercises 14 h, computer exercises 14 h and laboratory work 2 x 4 h. The course might be given in English.