Adaptive Filters (IEEE Press)
Adaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. This book enables 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 solutions.
Why Read This Book
You should read this book if you want a modern, rigorous treatment of adaptive filtering that balances theory, algorithm derivation, and practical MATLAB projects. You will gain deep understanding of LMS/RLS-class algorithms, mean‑square performance analysis, and contemporary extensions such as subband, sparse, and distributed adaptive filters.
Who Will Benefit
Graduate students, DSP engineers, and researchers who need a solid theoretical foundation plus practical algorithmic guidance for designing and analyzing adaptive filtering systems.
Level: Advanced — Prerequisites: Linear algebra, probability & random processes, signals & systems (discrete-time), basic numerical methods, and familiarity with MATLAB.
Key Takeaways
- Implement classical adaptive algorithms such as LMS, NLMS, affine-projection, and RLS from first principles.
- Perform mean-square and convergence analyses to predict steady-state error, transient behavior, and stability bounds.
- Design and tune adaptive filters for common tasks: system identification, echo/noise cancellation, and channel equalization.
- Apply and extend adaptive filtering concepts to subband, sparse, constrained, and networked (diffusion) settings.
- Use MATLAB exercises and projects to validate theoretical results and prototype real-world adaptive systems.
Topics Covered
- Part I – Introduction and Motivation
- Part II – Mathematical Preliminaries (linear algebra, stochastic processes)
- Part III – The LMS Algorithm and Basic Variants
- Part IV – Mean‑Square Analysis and Transient/Steady‑State Behavior
- Part V – Recursive Least Squares (RLS) and Fast Implementations
- Part VI – Stability, Robustness, and Tracking Performance
- Part VII – Normalized, Affine‑Projection, and Other Algorithmic Extensions
- Part VIII – Subband and Multi‑rate Adaptive Filtering
- Part IX – Sparse, Constrained, and Regularized Adaptive Filters
- Part X – Distributed/Diffusion Adaptive Networks and Cooperative Filtering
- Part XI – Applications, MATLAB Projects, Bibliographic Notes, and Problems
Languages, Platforms & Tools
How It Compares
Covers similar core material to S. Haykin's Adaptive Filter Theory but is more modern and mathematically systematic, with stronger emphasis on mean‑square analysis, MATLAB projects, and recent topics such as distributed and sparse adaptive filters.
