Course syllabus

## EITN60, 7,5 credits, A (Second Cycle)

Valid for: 2014/15
Decided by: Education Board A
Date of Decision: 2014-04-07

## General Information

Elective for: BME4-sbh, C4-ssr, D4-ssr, E4-bg, E4-ssr, 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 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 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

• 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

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.

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.

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: ETT042, ETTN05