Adaptive Filtering Algorithms and Practical Implementation

Diniz Paulo S R

ISBN 10: 0387312749 / ISBN 13: 9780387312743

Published by Springer, New York, 2008

About this Item

From an academic library with the usual stamps etc. This item is heavy and will attract postal surcharges. Seller Inventory # PF2184

Bibliographic Details

Title: Adaptive Filtering Algorithms and Practical ...

Publisher: Springer, New York

Publication Date: 2008

Binding: Hard Cover

Condition: Good

Dust Jacket Condition: No Jacket

Edition: Third Edition.

About this title

Synopsis:

This book presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner, using clear notations that facilitate actual implementation. Important algorithms are described in detailed tables which allow the reader to verify learned concepts. The book covers the family of LMS and algorithms as well as set-membership, sub-band, blind, IIR adaptive filtering, and more. The book is also supported by a web page maintained by the author.

From the Back Cover:

Adaptive Filtering: Algorithms and Practical Implementation, Third Edition, presents basic concepts of adaptive signal processing and filtering in a concise and straightforward manner. It concentrates on on-line algorithms whose adaptation occurs whenever a new sample of each environment signal is available. The material also illustrates block algorithms using a sub-band filtering framework whose adaptation occurs when a new block of data is available. Highlights of the new edition include: Expanded treatment of complex algorithms throughout the book New chapters on Data-Selective and Blind Adaptive Filtering An enlarged discussion of linear-constrained Wiener filters Detailed analysis of the affine projection algorithm Updated derivations and many new examples A primer on Kalman filtering in Appendix D as a complement to RLS algorithms. Algorithms are presented in a unified framework using a consistent notation that facilitates their actual implementation. The main algorithms are summarized and described in tables. Many examples address problems drawn from actual applications. The family of LMS and RLS algorithms as well as set-membership, sub-band, blind, nonlinear and IIR adaptive filtering, are covered. Problems are included at the end of chapters. Adaptive Filtering: Algorithms and Practical Implementation, Third Edition, is intended for advanced undergraduate and graduate students studying adaptive filtering and will also serve as an up-to-date and useful reference for professional engineers working in the field.