Fundamentals of Adaptive Filtering

This book is based on a graduate level course offered by the author at UCLA and has been classed tested there and at other universities over a number of years. This will be the most comprehensive book on the market today providing instructors a wide choice in designing their courses. Offers computer problems to illustrate real life applications for students and professionals alike An Instructor’s Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department. An Instructor’s Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Table of Contents

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