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 1:303:00pm, ECE/MP 30 (Auditorium 2).
Office hours
Sundeep Prabhakar Chepuri,
Tuesdays 3:103:30pm, ECE/MP 128
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
Midterm exam on October 1, 2019: 20%
Written exam. A4 cheat sheet will be allowed.
Syllabus covered till 17092019, i.e., till and including Steepestdescent method.
Final exam on December 4, 2019 between 25pm ECE/MP 30: 50%
Schedule
Lecture number  Date  Topic  Reference  Slides 
1  08082019  Introduction   slides 
2  13082019  Linear algebra   slides 
3  15082019  Indepedence day  NO CLASS    
4  20082019  Linear algebra  contd.   slides 
5  22082019  Optimization theory and random processes   slides 
6  29082019  Optimal estimation  T1  Ch. 1, Ch. 2.12.2  slides 
7  03092019  Linear estimation  T1  Ch. 3 and Ch. 4  slides 
8  5092019  Linear models  T1  Ch. 5  slides 
9  6092019  Constrained estimation and beamforming  T1. Ch.6.16.4 and 6.5  slides 
10  10092019  Exercise session  T1: Examples 4.1,4.2, 5.5; Problems I.14, II.7, II.17, II.29  
11  12092019  Steepest gradient descent  T1. Ch 8 (T1_1. Ch.4)  slides 
12  17092019  Steepest gradient descent contd.  T1. Ch 9 (T1_1. Ch.4)  slides 
13  19092019  LMS  T1. Ch. 10 (except 10.7); T1_1 Ch. 5.15.4  
14  24092019  LMS contd.  T1. Ch. 11 and Ch. 13 (except 10.7);Ch 5.6 and Ch. 5.8  
16  26092019  Constant modulus algorithm  T1. Ch. 12.2; T1_1. Ch. 5.7.2  
17  1102019  Midterm exam   
18  1102019  Midterm solutions, Leaky and Sign LMS  T1. Ch 12.1 ; T1_1. Ch. 5.7.1  
19  3102019  Recursive least squares  T1. Ch 14 ; T1_1. Ch. 12  
20  15102019  Kalman filter and weighted least squares  T1. Ch 7; T1_1. Ch. 2.C  slides 
21  17102019  Kalman filter and recursive least squares  T1. Ch 31; T1_1. Ch. 12.A  
22  22102019  Factor analysis and covariance estimation from subsampling  not included for exam  
23  24102019  Graph signal processing  not included for exam  
24  29102019  Echo cancellation and adaptive noise canceller  exercise session  
25  5112019  No class   
26  7112019  No class   
27  12112019  Holiday   
28  14112019  No class   
29  31102019  Total least squares and total least mean squares  T1_1. Ch. 17.B  
30  19112019  Adaptive linearly constrained beamforming   paper 
31  19112019  Adaptive linearly constrained beamforming   paper 
32  21102019  Recap   
33  22102019  Finite precision effects of LMS and RLS  

Previous exams
