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 1:30-3:00pm, ECE/MP 30 (Auditorium 2).

Office hours

Sundeep Prabhakar Chepuri,
Tuesdays 3:10-3:30pm, ECE/MP 128

Syllabus

Review of linear algebra and random processes. Optimal estimation. Linear estimation. Steepest-descent algorithms. Stochastic-gradient algorithms. Least squares and recursive least squares. Kalman filtering. Particle filtering. Blind deconvolution and beamforming. Subspace tracking. Robust adaptive filters. Iterative solvers of large-scale 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: Next-Generation Solutions, Tulay Adali and Simon Haykin, Wiley-India edition, 2010.

    • Adaptive Filter Theory, Simon Haykin, fourth edition, Pearson India, 2002.

Course requirements and grading

  • Three homeworks (programming): 10% each, i.e., 30% in total

    • Mandatory to participate in the final exam and to pass the course.

    • Prepare reports using LaTeX.

    • Submit only pdf files. Include Matlab scripts as appendices. Word documents will not be graded.

    • Late submissions are allowed, but will not be graded.

  • Midterm exam on October 1, 2019: 20%

    • Written exam. A4 cheat sheet will be allowed.

    • Syllabus covered till 17-09-2019, i.e., till and including Steepest-descent method.

  • Final exam on December 4, 2019 between 2-5pm ECE/MP 30: 50%

    • Written exam. A4 cheat sheet will be allowed.

    • Needless to say, includes the entire syllabus.

Schedule

Lecture numberDate Topic Reference Slides
1 08-08-2019 Introduction slides
2 13-08-2019 Linear algebra slides
315-08-2019 Indepedence day - NO CLASS -
420-08-2019 Linear algebra - contd. slides
522-08-2019 Optimization theory and random processes slides
629-08-2019 Optimal estimation T1 - Ch. 1, Ch. 2.1-2.2 slides
703-09-2019 Linear estimation T1 - Ch. 3 and Ch. 4 slides
85-09-2019 Linear models T1 - Ch. 5 slides
96-09-2019 Constrained estimation and beamforming T1. Ch.6.1-6.4 and 6.5 slides
1010-09-2019 Exercise session T1: Examples 4.1,4.2, 5.5; Problems I.14, II.7, II.17, II.29
1112-09-2019 Steepest gradient descent T1. Ch 8 (T1_1. Ch.4) slides
1217-09-2019 Steepest gradient descent contd. T1. Ch 9 (T1_1. Ch.4) slides
1319-09-2019 LMS T1. Ch. 10 (except 10.7); T1_1 Ch. 5.1-5.4
1424-09-2019 LMS contd. T1. Ch. 11 and Ch. 13 (except 10.7);Ch 5.6 and Ch. 5.8
1626-09-2019 Constant modulus algorithm T1. Ch. 12.2; T1_1. Ch. 5.7.2
171-10-2019 Mid-term exam
181-10-2019 Mid-term solutions, Leaky and Sign LMS T1. Ch 12.1 ; T1_1. Ch. 5.7.1
193-10-2019 Recursive least squares T1. Ch 14 ; T1_1. Ch. 12
2015-10-2019 Kalman filter and weighted least squares T1. Ch 7; T1_1. Ch. 2.C slides
2117-10-2019 Kalman filter and recursive least squares T1. Ch 31; T1_1. Ch. 12.A
2222-10-2019 Factor analysis and covariance estimation from subsampling not included for exam
2324-10-2019 Graph signal processing not included for exam
2429-10-2019 Echo cancellation and adaptive noise canceller exercise session
255-11-2019 No class
267-11-2019 No class
2712-11-2019 Holiday
2814-11-2019 No class
2931-10-2019 Total least squares and total least mean squares T1_1. Ch. 17.B
3019-11-2019 Adaptive linearly constrained beamforming paper
3119-11-2019 Adaptive linearly constrained beamforming paper
3221-10-2019 Recap
3322-10-2019 Finite precision effects of LMS and RLS

Previous exams