Ali H. Sayed is Professor of Electrical Engineering at UCLA, where he established and directs the Adaptive Systems Laboratory. He is a Fellow of the IEEE for his contributions to adaptive filtering and estimation algorithms. His research has attracted several recognitions including the 2003 Kuwait Prize, 2005 Terman Award, and several IEEE Best Paper Awards.

Adaptive Filters
Adaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. Now, preserving the style and main features of the earlier award-winning publication, Fundamentals of Adaptive Filtering (2005 Terman Award), the author offers readers and instructors a concentrated, systematic, and up-to-date treatment of the subject in this valuable new book.
Adaptive Filters allows readers to gain a gradual and solid introduction to the subject, its applications to a variety of topical problems, existing limitations, and extensions of current theories. The book consists of eleven parts-each part containing a series of focused lectures and ending with bibliographic comments, problems, and computer projects with MATLAB(r) solutions available to all readers. Additional features include:
*
Numerous tables, figures, and projects
*
Special focus on geometric constructions, physical intuition, linear-algebraic concepts, and vector notation
*
Background material on random variables, linear algebra, and complex gradients collected in three introductory chapters
*
Complete solutions manual available for instructors
*
MATLAB(r) solutions available for all computer projects
Adaptive Filters offers a fresh, focused look at the subject in a manner that will entice students, challenge experts, and appeal to practitioners and instructors.

Preface and Acknowledgments.
Notation and Symbols.
BACKGROUND MATERIAL.
A. Random Variables.
B. Linear Algebra.
C. Complex Gradients.
PART I: OPTIMAL ESTIMATION.
1. Scalar-Valued Data.
2. Vector-Valued Data.
Summary and Notes.
Problems and Computer Projects.
PART II: LINEAR ESTIMATION.
3. Normal Equations.
4. Orthogonality Principle.
5. Linear Models.
6. Constrained Estimation.
7. Kalman Filter.
Summary and Notes.
Problems and Computer Projects.
PART III: STOCHASTIC GRADIENT ALGORITHMS.
8. Steepest-Descent Technique.
9. Transient Behavior.
10. LMS Algorithm.
11. Normalized LMS Algorithm.
12. Other LMS-Type Algorithms.
13. Affine Projection Algorithm.
14. RLS Algorithm.
Summary and Notes.
Problems and Computer Projects.
PART IV: MEAN-SQUARE PERFORMANCE.
15. Energy Conservation.
15.A Interpretations of the Energy Relation.
16. Performance of LMS.
17. Performance of NLMS.
17.A Relating NLMS to LMS.
18. Performance of Sign-Error LMS.
19. Performance of RLS and Other Filters.
20. Nonstationary Environments.
21. Tracking Performance.
Summary and Notes.
Problems and Computer Projects.
PART V: TRANSIENT PERFORMANCE.
22. Weighted Energy Conservation.
23. LMS with Gaussian Regressors.
23.A Convergence Time.
24. LMS with non-Gaussian Regressors.
24.A Independence and Averaging Analysis.
25. Data-Normalized Filters.
25.A Stability Bound.
25.B Stability of NLMS.
Summary and Notes.
Problems and Computer Projects.
PART VI: BLOCK ADAPTIVE FILTERS.
26. Transform Domain Adaptive Filters.
26.A DCT-Transformed Regressors.
27. Efficient Block Convolution.
28. Block and Subband Adaptive Filters.
28.A Another Constrained DFT Block Filter.
28.B Overlap-Add Block Adaptive Filters.
Summary and Notes.
Problems and Computer Projects.
PART VII: LEAST-SQUARES METHODS.
29. Least-Squares Criterion.
30. Recursive Least-Squares.
31. Kalman Filtering and RLS.
31.A Extended RLS Algorithms.
32. Order and Time-Update Relations.
Summary and Notes.
Problems and Computer Projects.
PART VIII: ARRAY ALGORITHMS.
33. Norm and Angle Preservation.
34. Unitary Transformations.
35. QR and Inverse QR Algorithms.
35.A Array Algorithms for Kalman Filtering.
Summary and Notes.
Problems and Computer Projects.
PART IX: FAST RLS ALGORITHMS.
36. Hyperbolic Rotations.
37. Fast Array Algorithm.
37.A Chandrasekhar Filter.
38. Regularized Prediction Problems.
39. Fast Fixed-Order Filters.
Summary and Notes.
Problems and Computer Projects.
PART X: LATTICE FILTERS.
40. Three Basic Estimation Problems.
41. Lattice Filter Algorithms.
42. Error-Feedback Lattice Filters.
43. Array Lattice Filters.
Summary and Notes.
Problems and Computer Projects.
PART XI: ROBUST FILTERS.
44. Indefinite Least-Squares.
44.A Stationary Points.
44.B Inertia Conditions.
45. Robust Adaptive Filters.
45.A H1 Filters.
46. Robustness Properties.
Summary and Notes.
Problems and Computer Projects.
REFERENCES AND INDICES.
References.
Author Index.
Subject Index.