EE 662 – Spring 2007
Ali H. Sayed, "Fundamentals of Adaptive Filtering" IEEE Press 2003
Adaptive filters are systems that respond to variations in their environment by adapting their internal structure in order to meet certain performance specifications. Such systems are widely used in communications, signal processing, and control. This course will introduce the fundamental concepts in the design and analysis of adaptive filters. Roughly speaking the course is divided into three parts. The first part introduces the problem of (non-adaptive) linear estimation. The second part introduces the class of stochastic gradient algorithms while the third part focuses on recursive least squares. The course will various tools in linear algebra and multivariate Gaussian random variables. The application and project part of the course will deal with signal processing problems in geophysics and oil exploration and will be done in collaboration with Schlumberger Dhahran Center for Carbonate Research.
The project will be done in collaboration with Dr. Wail Mousa and Dr. Cesar Barajas-Olalde, Schlumberger Dhahran Center for Carbonate Research.