Adaptive Filters: Theory and Applications
This second edition of Adaptive Filters: Theory andApplications has been updated throughout to reflect the latestdevelopments in this field; notably an increased coverage given tothe practical applications of the theory to illustrate the muchbroader range of adaptive filters applications developed in recentyears. The book offers an easy to understand approach to the theoryand application of adaptive filters by clearly illustrating how thetheory explained in the early chapters of the book is modified forthe various applications discussed in detail in later chapters.This integrated approach makes the book a valuable resource forgraduate students; and the inclusion of more advanced applicationsincluding antenna arrays and wireless communications makes it asuitable technical reference for engineers, practitioners andresearchers.
Key features:
Offers a thorough treatment of the theory of adaptivesignal processing; incorporating new material on transform domain,frequency domain, subband adaptive filters, acoustic echocancellation and active noise control.
Provides an in-depth study of applications which nowincludes extensive coverage of OFDM, MIMO and smart antennas.
Contains exercises and computer simulation problems atthe end of each chapter.
Includes a new companion website hosting MATLAB(R)simulation programs which complement the theoretical analyses,enabling the reader to gain an in-depth understanding of thebehaviours and properties of the various adaptive algorithms.
Why Read This Book
You should read this book if you want a coherent bridge from adaptive-filter theory to a wide range of practical applications — from noise cancellation to antenna arrays and wireless receivers. It explains convergence and performance in a way that helps you implement and tune LMS/RLS-family algorithms and their modern variants with MATLAB examples and application-focused discussion.
Who Will Benefit
Graduate students and practicing DSP engineers working on adaptive filters, system identification, communications receivers, radar/array processing, or audio/speech enhancement.
Level: Advanced — Prerequisites: Undergraduate signals and systems, basic digital signal processing, linear algebra, probability and random processes; familiarity with MATLAB recommended.
Key Takeaways
- Implement and tune common adaptive algorithms (LMS, NLMS, RLS and affine-projection variants).
- Analyze convergence, steady-state error, and computational trade-offs of adaptive filters.
- Design adaptive solutions for noise cancellation, echo cancellation, and system identification.
- Apply adaptive-array and beamforming techniques to antenna arrays and wireless receivers.
- Develop and evaluate blind/adaptive equalization and blind source separation methods.
- Translate theoretical results into MATLAB simulations and practical implementation insights.
Topics Covered
- 1. Introduction and Motivation for Adaptive Filtering
- 2. Statistical Preliminaries and Wiener Filter Theory
- 3. Stochastic Gradient and the LMS Family
- 4. Normalized LMS, Transform-Domain and Affine Projection Algorithms
- 5. Recursive Least-Squares (RLS) Algorithms and Variants
- 6. Convergence Analysis and Mean-Square Performance
- 7. Subband and Multirate Adaptive Filtering
- 8. Blind Adaptive Methods and Equalization
- 9. Adaptive Array Processing and Beamforming
- 10. Applications to Communications, Radar and Audio/Speech
- 11. Implementation Issues, Computational Complexity and Numerical Aspects
- 12. MATLAB Examples, Simulations and Practical Tips
Languages, Platforms & Tools
How It Compares
Covers similar ground to S. Haykin's "Adaptive Filter Theory" and S. Haykin/S. Sayed's treatments, but Farhang-Boroujeny's text is more application-oriented with clear links from theory to modern applications like arrays and wireless systems.
