EE 535 Adaptive Filter Theory

Catalog Data:Study of the mathematical theory of various realizations of linear filters. Detailed study of linear optimum filtering, namely Wiener filtering, linear prediction, and Kalman filtering. FIR structures versus lattice filter structures. Method of least Squares, Comparative study of steepest descent, least-mean square (LMS) and recursive least squares (RLS) filter design algorithms.

Textbook: Adaptive Filter Theory, 4/E , Simon Haykin, Prentice Hall, 2002.

Instructor : Dr. Mustafa A. Altinkaya, Assistant Professor
Class Hours : Thursday 09:45 a.m. - 00:30 p.m.
Prerequisites: Introductory undergraduate courses on probability theory and digital signal processing. Undergraduate level background on communication and control systems is advantageous.

Topics:

  1. Stochastic Processes and Models
  2. Wiener Filters
  3. Linear Prediction
  4. Method of Steepest Descent
  5. Least-Mean-Square Adaptive Filters
  6. Normalized Least-Mean-Square Adaptive Filters
  7. Method of Least Squares
  8. Recursive Least-Square Adaptive Filters
  9. Kalman Filters
  10. Square-Root Adaptive Filters
  11. Frequency-Domain and Subband Adaptive Filters (tentative)
Homeworks : There are 4-6 homeworks.

Grading : homeworks 10%, midterm exam 25%, term project 25%, final 40%.

Mustafa A. ALTINKAYA 2009