Fundamentals of Adaptive Filtering (IEEE Press)
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.
Why Read This Book
You should read this book if you want a rigorous, unified foundation in adaptive filtering: it derives LMS, RLS, Kalman and projection-based algorithms from first principles and gives you the mean-square-performance tools needed to analyze and design adaptive systems. It also includes numerous exercises and computer problems so you can move from theory to practical implementation and evaluation.
Who Will Benefit
Graduate students, researchers, and practicing DSP engineers who design or analyze adaptive systems (echo cancellation, equalization, noise cancellation, channel tracking) and need a rigorous, mathematically sound reference.
Level: Advanced — Prerequisites: Linear algebra (matrix analysis), probability and random processes, basic DSP (LTI systems, Fourier transforms), and calculus.
Key Takeaways
- Derive optimal linear estimators (Wiener) and relate them to adaptive algorithms
- Analyze transient and steady-state mean-square performance of stochastic-gradient and RLS algorithms
- Implement and compare LMS variants (NLMS, transform-domain) and RLS-family algorithms
- Apply state-space and Kalman filtering perspectives to adaptive filtering and tracking problems
- Design and tune adaptive filters for practical tasks such as echo cancellation, channel equalization, and noise suppression
- Evaluate algorithm complexity, stability, and tracking behavior and select appropriate algorithms for given scenarios
Topics Covered
- Introduction and Preliminaries
- Optimal Linear Estimation and the Wiener Filter
- Performance Surfaces and Mean-Square Error Criteria
- Stochastic Gradient Algorithms: LMS and Variants
- Normalized & Transform-Domain LMS Methods
- Recursive Least Squares (RLS) Algorithms and Fast Implementations
- State-Space Methods and Kalman Filtering
- Affine Projection and Subspace Methods
- Convergence, Tracking, and Stability Analysis
- Practical Issues, Implementation Considerations, and Computer Problems
Languages, Platforms & Tools
How It Compares
Covers much the same core material as Haykin's Adaptive Filter Theory but is more modern and mathematically unified (strong emphasis on mean-square analysis and state-space views); more rigorous than older texts like Widrow & Stearns.
