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EE 662 -
Adaptive Filtering and Applications
(KFUPM)
EE 343 - Adaptive Signal Processing (KAUST)
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Announcements |
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May 08, 2010: Problem Session IV has been uploaded. [zip]
May 18, 2010: Problem Session V has been uploaded. [zip]
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Administrative Info |
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Instructor: Dr. Tareq Al-Naffouri,
naffouri@kfupm.edu.sa
tareq.alnaffouri@kaust.edu.sa
Office hours:
Sun & Tue 10:00 AM -11:30 AM (KFUPM)
Wed 1:00 PM -2:30 PM (KAUST)
Course website:
http://faculty.kfupm.edu.sa/ee/naffouri/courses/ee662.html
Lectures:
| KFUPM |
KAUST |
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Sun: 05:00 PM - 06:15 PM |
Sat: 10:30
AM - 12:00 PM |
| Tue: 05:00
PM - 06:15 PM |
Wed: 01:00 PM - 02:30
PM |
| Room 1008,
Bldg. 59 |
Room 3120, Bldg. 9 |
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Course Description |
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Adaptive
filters are systems that respond to variations in their
environment by adapting their internal structure in order to
meet certain performance specifications. Such systems are widely
used in communications, signal processing, and control. This
course will introduce the fundamental concepts in the design and
analysis of adaptive filters. Roughly speaking the course is
divided into three parts. The first part introduces the problem
of (non-adaptive) linear estimation. The second part introduces
the class of stochastic gradient algorithms while the third part
focuses on recursive least squares. The course will also cover
various tools in linear algebra and multivariate Gaussian random
variables.
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Course Outline |
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- Week 01: Introduction, Review and background
- Week 02: Linear estimation and Wiener filters
- Week 03: Constrained linear estimation
- Week 04: Steepest descent algorithms
- Week 05: Stochastic gradient algorithms 1
- Week 06: Stochastic gradient algorithms 2
- Week 07: Steady state and tracking performance of adaptive filters
(1)
- Week 08: Steady state and tracking performance of adaptive filters
(2)
- Week 09: Transient performance of adaptive filters
- Week 10: The least-squares criterion
- Week 11: Recursive least-squares (1)
- Week 12: Recursive least-squares (2)
- Week 13: RLS array algorithms
- Week 14: Project presentations
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Text
Book |
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Ali H. Sayed, “Adaptive
Filters,” John Wiley & Sons 2008
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Additional References |
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Homework Assignments |
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Homework will be assigned once every
two weeks. Collaboration is encouraged between students in all
matter of the course. However, each student should submit his own
homework.
Homework 1 [pdf]
Homework 2 [pdf]
Homework 3 [KAUST]
[KFUPM]
Homework 4 [KAUST]
[KFUPM]
Homework 5 [KAUST]
[KFUPM]
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Major
Exams |
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Major Exam 1
March 21st, 2010 [Solution]
Major Exam 2
May 8th, 2010
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Reading Material |
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Topic |
Sayed
2008 |
Sayed
2003 |
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Steepest
Descent |
Chapter 08 |
Sections 4.1 and 4.2 |
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Transient Behavior |
Chapter 09 (except 9.6 and 9.7) |
Sections 4.3 and 4.5 (except 4.3.4) |
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LMS Algorithm |
Chapter 10 (except 10.4 and 10.7) |
Sections 5.2, 5.3, 5.4, and .10 (except 5.2.3) |
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NLMS Algorithm |
Chapter 11 (except 11.4) |
Section 5.6 (except 5.6.3) |
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RLS Algorithm |
Chapter 14 |
Section 5.9 |
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LS Criterion |
Chapter 29 (except 29.3) |
Sections 11.1, 11.2, 11.3, and 11.4 |
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RLS |
Chapter 30 |
Sections 12.1, 12.2, and 12.3 |
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Projects |
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S. No. |
Projects |
Description |
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1 |
Time-varying
DOA estimation for multiple moving targets |
[pdf]
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Estimation of
time-varying multipath channel parameters in OFDM signal |
[pdf]
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Estimation of
channel parameters in UWB impulse radio communication |
[pdf]
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De-noising
compressed estimates of random sparse vectors |
[pdf]
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Performance
analysis of diffusion Least Mean Squares over adaptive
networks |
[pdf]
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Mean
convergence analysis of NLMS and e-NLMS |
[pdf]
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Impulse noise
estimation and cancelation in DSL using block compressive
sensing |
[pdf]
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Channel
Estimation using adaptive compressive sensing |
[pdf]
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9 |
Data-aided Channel Estimation
in OFDM Systems using Kalman Filter |
[pdf] |
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Problem
Sessions |
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Problem Session I [pdf]
Problem Session II [zip]
Problem Session III [Part1.zip]
[Part2.zip]
Problem Session IV [zip]
Problem Session V [zip]
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Grading Policy (Tentative) |
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Students will be assigned grades on
the following basis:
| Quizzes and Homeworks |
20 % |
| Major Exam 1 |
15 % |
| Major Exam 2 |
15 % |
| Project |
20 % |
| Final Exam |
30 % |
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