E9 211 Adaptive Signal Processing
About E9 211 Adaptive Signal Processing
The primary aim of E9 211 is to develop a mathematical theory of linear adaptive filters. Adaptation is accomplished by adjusting the free parameters of a filter according to the input data to achieve the desired output. Such adaptive algorithms are frequently encountered in many signal processing and machine learning algorithms. The adaptive signal processing course provides a comprehensive treatment of mathematical signal processing algorithms for designing optimum and linear filters; designing, implementing, and analyzing adaptive filters applied to system identification, inverse modeling (deconvolution), adaptive control, and interference cancellation; and some selected emerging topics in signal processing.
Prerequisite
Undergraduate courses on probability theory, digital signal processing, and linear algebra. First few lectures provide the required background in linear algebra and random processes.
Lectures
Tuesdays and Thursdays 11:30am1:00pm, Online via MS Teams.
Syllabus
Review of linear algebra and random processes. Optimal estimation. Linear estimation. Steepestdescent algorithms. Stochasticgradient algorithms. Least squares and recursive least squares. Kalman filtering. Particle filtering. Blind deconvolution and beamforming. Subspace tracking. Robust adaptive filters. Iterative solvers of largescale linear systems. Selected emerging topics.
Textbooks
T1. Adaptive Filters, by Ali H. Sayed, John Wiley & Sons, NJ, 2008
T1_1. Fundamentals of Adaptive Filtering, by Ali H. Sayed, Wiley student edition,2016
T2. Adaptive Signal Processing, by Bernard Widrow, Pearson, 2002
Other useful resources
Adaptive Signal Processing: NextGeneration Solutions, Tulay Adali and Simon Haykin, WileyIndia edition, 2010.
Adaptive Filter Theory, Simon Haykin, fourth edition, Pearson India, 2002.
Course requirements and grading
Schedule
Lecture number  Date  Topic  Reference  Slides 
1  08102020  Introduction   slides 
2  13102020  Linear algebra   slides 
3  15102020  Linear algebra  contd.   slides 
4  20102020  Optimization theory and random processes   slides 
5  22102020  Exercise session   
6  27102020  Optimal estimation  T1  Ch. 1, Ch. 2.12.2  slides 
7  29102020  Linear estimation  T1  Ch. 3 and Ch. 4  slides 
8  03112020  Linear models  T1  Ch. 5  slides 
9  05112020  Constrained estimation and beamforming  T1. Ch.6.16.4 and 6.5  slides 
10  10112020  Exercise session  T1: Examples 4.1,4.2, 5.5; Problems I.14, II.7, II.17, II.29  
11  12112020  Direction estimation and beamforming   
12  17112020  Steepest gradient descent  T1. Ch 8 (T1_1. Ch.4)  slides 
13  19112020  Steepest gradient descent contd.  T1. Ch 9 (except 9.8) (T1_1. Ch.4)  slides 
14  24112020  Newton's method and LMS  T1. Ch. 9.8, 10 (except 10.7); T1_1 Ch. 5.15.4  slides slides 
15  26112020  LMS contd.  T1. Ch. 11 and Ch. 13 (except 10.7);Ch 5.6 and Ch. 5.8  slides 
16  01122020  Exercise session  T1: Problems III.1, III.8  
17  03122020  Midterm exam   
18  08122020  Midterm solutions, Leaky and Sign LMS  T1. Ch 12.1 ; T1_1. Ch. 5.7.1  
19  10122020  Recursive least squares  T1. Ch 14 ; T1_1. Ch. 12  
20  15122020  Kalman filter and weighted least squares  T1. Ch 7; T1_1. Ch. 2.C  slides 
21  17122020  Kalman filter and recursive least squares  T1. Ch 31; T1_1. Ch. 12.A  
22  22122020  Factor analysis and covariance estimation from subsampling  not included for exam  
23  24122020  Graph signal processing  not included for exam  
24  29122020  No class   
25  31122020  No class   
26  05012021  Adaptive linearly constrained beamforming   paper 
27  07012021  Finite precision effects of LMS and RLS   
28  12012021  Finite precision effects of LMS and RLS   
29  14012021  Recap  

Previous exams
