ISBN 9788132206729,Adaptive Filtering: Algorithms and Practical Implementation

Adaptive Filtering: Algorithms and Practical Implementation






Publication Year 2012

ISBN 9788132206729

ISBN-10 813220672X


Edition 3rd
Number of Pages 632 Pages
Language (English)


This book presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework using clear notations that facilitate actual implementation. The main algorithms are described in tables which are detailed enough to allow the reader to verify the learned concepts. Many examples address problems drawn from actual applications. This book covers the family of LMS and algorithms as well as set-membership, sub-band, blind, IIR adaptive filtering, and more. Several problems are included at the end of chapters and some of these problems address applications. A user-friendly MATLAB package is provided where the reader can easily solve new problems and test all of the algorithms in a fast manner. Additionally, the book provides easy access to working algorithms for practicing engineers. The book has a solutions manual that is available from the publisher for instructors. Another resource for instructors is a set of master transparencies as a MATLAB Package including the MATLAB codes for all the algorithms described in the text which is available from the author. The author also has a web page for the book.

Table of Contents

Introduction to Adaptive Filtering
Fundamentals of Adaptive Filtering
The Least-Mean-Square (LMS) Algorithm
LMS-Based Algorithms
Conventional RLS Adaptive Filter
Data-Selective Adaptive Filtering
Adaptive Lattice-Based RLS Algorithms
Fast Transversal RLS Algorithms
QR-Decomposition-Based RLS Filters
Adaptive IIR Filters
Nonlinear Adaptive Filtering
Subband Adaptive Filters
Blind Adaptive Filtering
Appendix A: Complex Differentiation
Appendix B: Quantization Effects in the LMS Algorithm
Appendix C: Quantization Effects in the RLS Algorithm
Appendix D: Kalman Filters.