From the reviews of the third edition: "This text concentrates on studying on-line algorithms, those whose adaptation occurs whenever a new sample of each environment signal is available. … This edition also includes basic introductions to nonlinear adaptive filtering and blind signal processing as natural extensions of the algorithms … . The book is of great importance for digital signal processing undergraduate and graduate courses in universities." (George S. Stavrakakis, Zentralblatt MATH, Vol. 1155, 2009) "The book titled ‘Adaptive Filtering: Algorithms and Practical Implementation,’ Third Edition, by Paulo S. R. Diniz replaces two previous editions. This new edition has improved significantly upon those two editions. … The book is also very practical. To attest to its suitability as a teaching text, the author has given a number of carefully developed examples which are useful … to students. … Overall, the book is excellent for teaching the subject of adaptive signal processing and enhances the landscape of textbooks on the subject." (Tokunbo Ogunfunmi, IEEE Communications Magazine, October, 2009)
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.