University of California, Santa Barbara
Department of Electrical and Computer Engineering


Adaptive Filter Theory

ECE 245 - Fall 2009

Instructor: Professor John J. Shynk

Schedule: Monday and Wednesday
10:00 - 11:50 a.m.
Phelps 1431


 

Announcements:

Office Hours:

Monday and Wednesday 2:00 - 3:00 p.m.

Course Information:

Theory and analysis of adaptive filters. Optimal filtering, linear prediction, method of least squares.
Steepest-descent and Newton search methods, gradient estimation, LMS adaptive algorithm, recursive least squares.
Gradient and least-squares lattice algorithms for joint-process estimation. Convergence analysis, stability conditions,
time constants, misadjustment.

Grading:

Homework Problems (weekly), 20%
Programming Assignment, 30% (Thursday, December 3, 5:00 p.m.)
Final Exam (open book), 50% (Tuesday, December 8, 8:00 - 11:00 a.m.)

Required Text:

Haykin, Adaptive Filter Theory, Prentice-Hall, 4th Edition, 2002

Recommended Text:

Widrow and Stearns, Adaptive Filter Processing, Prentice-Hall, 1985

Lecture Handouts:

 


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Last Updated: December 5, 2009