
EE 662 
Adaptive Filtering and Applications
(KFUPM)
EE 343  Adaptive Signal Processing (KAUST)





Announcements 

May 08, 2010: Problem Session IV has been uploaded. [zip]
May 18, 2010: Problem Session V has been uploaded. [zip]




Administrative Info 

Instructor: Dr. Tareq AlNaffouri,
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 
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 




Course Description 

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 (nonadaptive) 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.




Course Outline 

 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 leastsquares criterion
 Week 11: Recursive leastsquares (1)
 Week 12: Recursive leastsquares (2)
 Week 13: RLS array algorithms
 Week 14: Project presentations




Text
Book 

Ali H. Sayed, “Adaptive
Filters,” John Wiley & Sons 2008




Additional References 





Homework Assignments 

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]




Major
Exams 

Major Exam 1
March 21st, 2010 [Solution]
Major Exam 2
May 8th, 2010




Reading Material 

Topic 
Sayed
2008 
Sayed
2003 
Steepest
Descent 
Chapter 08 
Sections 4.1 and 4.2 
Transient Behavior 
Chapter 09 (except 9.6 and 9.7) 
Sections 4.3 and 4.5 (except 4.3.4) 
LMS Algorithm 
Chapter 10 (except 10.4 and 10.7) 
Sections 5.2, 5.3, 5.4, and .10 (except 5.2.3) 
NLMS Algorithm 
Chapter 11 (except 11.4) 
Section 5.6 (except 5.6.3) 
RLS Algorithm 
Chapter 14 
Section 5.9 
LS Criterion 
Chapter 29 (except 29.3) 
Sections 11.1, 11.2, 11.3, and 11.4 
RLS 
Chapter 30 
Sections 12.1, 12.2, and 12.3 




Projects 

S. No. 
Projects 
Description 
1 
Timevarying
DOA estimation for multiple moving targets 
[pdf]

2 
Estimation of
timevarying multipath channel parameters in OFDM signal 
[pdf]

3 
Estimation of
channel parameters in UWB impulse radio communication 
[pdf]

4 
Denoising
compressed estimates of random sparse vectors 
[pdf]

5 
Performance
analysis of diffusion Least Mean Squares over adaptive
networks 
[pdf]

6 
Mean
convergence analysis of NLMS and eNLMS 
[pdf]

7 
Impulse noise
estimation and cancelation in DSL using block compressive
sensing 
[pdf]

8 
Channel
Estimation using adaptive compressive sensing 
[pdf]

9 
Dataaided Channel Estimation
in OFDM Systems using Kalman Filter 
[pdf] 




Problem
Sessions 

Problem Session I [pdf]
Problem Session II [zip]
Problem Session III [Part1.zip]
[Part2.zip]
Problem Session IV [zip]
Problem Session V [zip]




Grading Policy (Tentative) 

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 % 










