Information Package / Course Catalogue - Adnan Menderes University

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Adaptive Signal Processing

Course Code	: EEE542
Course Type	: Area Elective
Couse Group	: Second Cycle (Master's Degree)
Education Language	: Turkish
Work Placement	: N/A

Theory	: 3
Prt.	: 0
Credit	: 3
Lab	: 0
ECTS	: 6

Objectives of the Course

To be introduced of different adaptive filtering methods and to improve the usage qualification in estimator design. Analyzes of the performances and the comparions of these methods and of the optimal design methods will be investigated.

Course Content

Mean Square Estimation Techniques, (Linear MSE estimation, optimal estimation), Filtering the Random Processes, Moving Average (MA), Auto-regressive (AR) and ARMA processes, Wiener Filtering (Solving Wiener-Hopf Equations), FIR, IIR, Causal IIR Wiener Filters, Iterative methods for the solution of Wiener-Hopf Equations, Adaptive Filters, LMS Filter, FIR, IIR , Normalized and other variations, RLS, Kalman Filter, Applications

Name of Lecturer(s)

Learning Outcomes

1.	For a given linear adaptive estimation problem and its requirements, choose appropriate adaptation methods.
2.	For a given linear adaptive estimation problem and its requirements, choose appropriate filter length.
3.	For a given linear adaptive estimation problem, identify relevant signals, express adaptation and filtering operations.
4.	Write adaptive filtering codes and compare the performances of adaptation methods.
5.	Correctly choose or decide on the strategy about the step size parameter according to the nature of the problem and/or computational environment.
6.	Propose ways to reduce computational load of algorithms.
7.	Propose ways to improve numerical stability of algorithms.

Recommended or Required Reading

1.	Monson H. Hayes, Statistical Digital Signal Processing and Modelling, John Wiley & Sons, 1996.
2.	Simon Haykin, Adaptive Filter Theory, Prentice Hall, 1996.

Weekly Detailed Course Contents

Week 1 - Theoretical

Review of Random Processes

Week 2 - Theoretical

Mean Square Estimation Techniques, (Linear MSE estimation, optimal estimation)

Week 3 - Theoretical

Filtering the Random Processes

Week 4 - Theoretical

Moving Average (MA), Auto-regressive (AR) and ARMA processes

Week 5 - Theoretical

Wiener Filtering (Solving Wiener-Hopf Equations)

Week 6 - Theoretical

FIR, IIR, Causal IIR Wiener Filters

Week 7 - Theoretical

Iterative methods for the solution of Wiener-Hopf Equations

Week 8 - Theoretical

Repeat chapters - Midterm Exam

Week 9 - Theoretical

Adaptive Filters

Week 10 - Theoretical

LMS Filter

Week 11 - Theoretical

FIR, IIR , Normalized and other variations

Week 12 - Theoretical

RLS Filter

Week 13 - Theoretical

Kalman Filters

Week 14 - Theoretical

Applications

Assessment Methods and Criteria

Type of Assessment	Count	Percent
Attending Lectures	1	%2
Assignment	5	%8
Midterm Examination	1	%20
Final Examination	1	%70

Workload Calculation

Activities	Count	Preparation	Time	Total Work Load (hours)
Lecture - Theory	14	1	3	56
Assignment	5	2	2	20
Individual Work	6	1	1	12
Midterm Examination	1	10	5	15
Final Examination	1	20	30	50
TOTAL WORKLOAD (hours)				153

Contribution of Learning Outcomes to Programme Outcomes

	PÇ-1	PÇ-2	PÇ-3	PÇ-4	PÇ-5	PÇ-6	PÇ-7
OÇ-1	4	4	4	4	4	4	4
OÇ-2	4	4	4	4	4	4	4
OÇ-3	4	4	4	4	4	4	4
OÇ-4	4	4	4	4	4	4	4
OÇ-5	4	4	4	4	4	4	4
OÇ-6	4	4	4	4	4	4	4
OÇ-7	4	4	4	4	4	4	4

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