COURSE UNIT TITLE

: ADAPTIVE FILTER THEORY

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EEE 5069 ADAPTIVE FILTER THEORY ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASISTANT PROFESSOR MEHMET EMRE ÇEK

Offered to

ELECTRICAL AND ELECTRONICS ENGINEERING NON -THESIS (EVENING PROGRAM)
ELECTRICAL AND ELECTRONICS ENGINEERING
Biomedical Tehnologies (English)
Industrial Ph.D. Program In Advanced Biomedical Technologies
ELECTRICAL AND ELECTRONICS ENGINEERING

Course Objective

The main purpose of this lesson is to provide the mathematical theory and various applications of linear adaptive filters by using recursive algortihms.

Learning Outcomes of the Course Unit

1   To be able to explain the linear optimum filters and linear filter structures used for filtering problem.
2   To be able to comprehend Wiener filter and Linear Prediction Methods which are basis for adaptive filtering.
3   To be able to describe the basic parts of Least Mean Square and Recursive Least Squares adaptive filtering techniques.
4   To be able to realize adaptive filtering for the signals observed from a nonlinear dynamical system by Extended and Unscented Kalman filters.
5   To be able to compare the given adaptive filtering techniques and clarify the advantage and disadvantage of each technique.
6   To be able to solve signal processing problems on computer environment by performing adaptive filtering techniques

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Background on Stochastic Processes and Random Signals
2 Wiener Filters
3 Linear Backward and Forward Prediction, Levinson Durbin Algorithm, Cholesky Factorization
4 Method of Steepest Descent
5 Least Mean Square Adaptive Filters (LMS), Comparison of LMS Algorithm with Steepest Descent Algorithm.
6 Project Study
7 Normalized Least Mean Square Adaptive Filters
8 Method of Least Squares
9 Recursive Least Squares Adaptive Filters (RLS)
10 Convergence Analysis of the RLS Algorithm
11 Project Study
12 Kalman Filters, Statement of the Kalman Filtering Problem
13 Extended and Unscented Kalman Filters
14 Discussions before Project Presentations

Recomended or Required Reading

Adaptive Filter Theory, Simon Haykin, Prentice Hall, Fourth Edition, 2002.

Planned Learning Activities and Teaching Methods

The teaching method is to explain the subjects to the students face to face. The student should be able to identify the engineering problems explained in the lesson and perform simulations associated with applications which constitute the learning method. For this purpose, students are responsible for term project near the end of term involving these simulations which corresponds to the grade of the lesson.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 PRJ PROJECT
2 FCG FINAL COURSE GRADE PRJ * 1

Further Notes About Assessment Methods

None

Assessment Criteria

Term Project

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

emre.cek@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 3 36
Preparation for project study 2 6 12
Preparing projects 5 3 15
Treating the project studies during the term 2 5 10
Treating the Projects 1 10 10
Preparations before/after weekly lectures 12 10 120
Project Study 2 3 6
TOTAL WORKLOAD (hours) 209

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.144
LO.2553
LO.3553
LO.4553
LO.5553
LO.645

CONTACT INFORMATION
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Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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