Fundamentals of Adaptive Filtering

A. H. Sayed, Fundamentals of Adaptive Filtering, Wiley, NJ, 2003. Recipient of 2005 Frederick E. Terman Award


This graduate-level textbook offers a comprehensive and up-to-date treatment of adaptive filtering; a vast and fast-moving field. The book is logically organized, specific in its presentation of each topic, and far-reaching in scope. Throughout the presentation, special emphasis is placed on geometric constructions, energy conservation arguments, system-theoretic arguments, and linear algebraic formulations.

The textbook offers a fresh, broad, and systematic treatment of of the field; it illustrates extensive commonalities that exist among different classes of adaptive algorithms and even among different filtering theories. The book also provides a uniform treatment of the subject matter, addressing some existing limitations, providing additional insights, and detailing extensions of current theory.

The book is designed to be self-contained, with careful attention given to appendices, problems, examples, and a variety of practical computer projects. The bibliography is up-to-date with extensive commentaries on how the contributions relate to each other in time and in context.

Each chapter includes concepts that reinforce the principles covered, bibliographic notes for further study, numerous problems that vary in difficulty and applications, computer projects that illustrate real-life applications, and several helpful appendices.

MATLAB programs that solve all computer projects are avaliable for download by all readers from the publisher’s website. The computer projects feature topics such as linear and decision feedback equalization, channel estimation, beamforming, tracking of fading channels, line and acoustic echo cancellation, active noise control, OFDM receivers, CDMA receivers, and even finite precision effects.

A complete solutions manual for all problems in the book is available to instructors upon request.


1. Optimal Estimation. 2. Linear Estimation. 3. Constrained Linear Estimation. 4. Steepest-Descent Algorithms. 5. Stochastic-Gradient Algorithms. 6. Steady-State Performance of Adaptive Filters. 7. Tracking Performance of Adaptive Filters. 8. Finite-Precision Effects. 9. Transient Performance of Adaptive Filters. 10. Block Adaptive Filters. 11. The Least-Squares Criterion. 12. Recursive Least-Squares. 13. RLS Array Algorithms. 14. Fast Fixed-Order Filters. 15. Lattice Filters. 16. Laguerre Adaptive Filters. 17. Robust Adaptive Filters.

About the Author:

A. H. Sayed is Professor of Electrical Engineering at UCLA. He is a Fellow of IEEE for his contributions to adaptive filtering and estimation algorithms.


The preface explains the distinctive features of the treatment, including the special roles played by geometric constructions, energy conservation arguments, system-theoretic arguments, uniformity, and extensive new material not covered elsewhere. The book can be used to design different course offerings according to instructor needs in terms of breadth and depth of coverage.