Adaptive Filtering ─ Fundamentals of Least Mean Squares With MATLAB

This book discusses the fundamentals of adapting filtering, with a focus on the most important part of the statistical signal processing area—the least-mean square (LMS) adaptive filter. Accordingly, the text provides an introduction to random variables and stochastic processes as well as vectors and matrices. It features robust appendices full of supporting material, plus numerous MATLABR functions and script files specifically developed to verify the presented theory and examples. End-of-chapter problems are included to provide the reader with a deeper understanding of key concepts.

Alexander D. Poularikas is chairman of the electrical and computer engineering department at the University of Alabama in Huntsville, USA. He previously held positions at University of Rhode Island, Kingston, USA and the University of Denver, Colorado, USA. He has published, coauthored, and edited 14 books and served as an editor-in-chief of numerous book series. A Fulbright scholar, lifelong senior member of the IEEE, and member of Tau Beta Pi, Sigma Nu, and Sigma Pi, he received the IEEE Outstanding Educators Award, Huntsville Section in 1990 and 1996. Dr. Poularikas holds a Ph.D from the University of Arkansas, Fayetteville, USA.