520.645 - Adaptive Filtering

520.645 Adaptive Filtering

Department of Electrical and Computer Engineering
The Johns Hopkins University

Instructor
- Prof. Trac D. Tran
  Address: 310 Barton Hall
  Phone: (410) 516-7416
  Email: trac@jhu.edu
  Office Hours : Thursday 1-3 or by appointment

Announcements
- Exam I: Thursday, Oct. 18, 10:30 - 12:00, Barton 117.
- Exam II: Friday, Dec. 14, 10:00 - 12:00, Barton 117.
- Make-up Lectures: Monday, 11/12/2001, and Monday, 11/19/2001, 5:30 - 7:00, Barton 225.

Homework Assignments
- Problem Set I. Postscript | PDF.
- Problem Set II. Postscript | PDF.
- Problem Set III. Postscript | PDF.
- Problem Set IV. Postscript | PDF.
- Problem Set V. Postscript | PDF.

Required Texts
- S. Haykin, Adaptive Filter Theory, Prentice Hall, Third Edition, 1996. ISBN 013322760X.
- P. S. R. Diniz, Adaptive Filtering: Algorithms and Practical Implementations, Kluwer Academic, 1997. ISBN 0792399129.

Additional References (on Library Reserve)
- B. Widrow and S. D. Stearns, Adaptive Signal Processing, Prentice Hall, 1985.
- J. R. Treichler, C. R. Johnson, Jr., and M. G. Larimore, Theory and Design of Adaptive Filters, Prentice Hall, 2001.

Course Description
- An introduction to the basic principles, mathematical theory, algorithmic design, and practical implementation of linear adaptive filters. Topics include adaptive least-mean-square and recursive-least-square algorithms, adaptive lattice structures, fast finite-precision implementations, and behavioral analysis.
- Prerequisites: 520.435, basic knowledge of probability and stochastic processes, Matlab programming.

Tentative Syllabus
- Introduction. Motivation. Applications.
- Fundamentals and Background Material.
  - Stationary Stochastic Processes. Signal Modeling and Representation.
  - Optimum Linear Filtering. Wiener Filters. Linear Prediction
  - Optimization Algorithms. Steepest Descent. Newton Method.
- Least-Mean-Square (LMS) Algorithm.
  - General Algorithm. Theory.
  - Analysis, Behavior, and Properties.
  - LMS Variants: Normalized LMS, Block Adaptive Filters. Subband Adaptive Filters.
- Recursive Least-Squares (RLS) Algorithm.
  - Least Squares.
  - General Algorithm. Theory.
  - Analysis, Behavior, and Properties.
  - RLS Variants: Fast Transversal RLS, QR-Based RLS.
  - Connection to Kalman Filters.
- Adaptive Lattice Filters.
  - RLS Lattice Algorithms.
  - Gradient Lattice Algorithms.
  - Analysis, Behavior, and Properties.

Ethics Issues
- Please read the information provided by the Ethics Board.
- Exams will be closed book and closed notes. One 8.5 x 11 handwritten formula sheet will be permitted.
- On homework and projects, you are permitted to discuss the problems for clarification purposes, and to help each other with specific points. However, the overall solution and write-up should be your own work.