Adaptive Filtering: Algorithms and Practical Implementation
This book presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner, using clear notations that facilitate actual implementation. Important algorithms are described in detailed tables which allow the reader to verify learned concepts. The book covers the family of LMS and algorithms as well as set-membership, sub-band, blind, IIR adaptive filtering, and more. The book is also supported by a web page maintained by the author.
Why Read This Book
You should read this book if you want a practical, implementation-oriented guide to modern adaptive filtering: it walks you through the main algorithms (LMS, RLS, set‑membership, subband, blind, IIR), explains performance tradeoffs, and provides algorithm tables and implementation notes that make translating theory into code straightforward. The author balances analysis and hands-on guidance, including finite‑precision and complexity discussions that are essential when moving algorithms onto real DSP hardware.
Who Will Benefit
Graduate students and practicing DSP engineers who already know basic signals and systems and need to design, analyze, and implement adaptive filters for communications, audio, or control applications.
Level: Advanced — Prerequisites: Undergraduate-level signals and systems, linear algebra, basic probability/statistics, familiarity with digital filters and MATLAB (or similar) for reproducing examples.
Key Takeaways
- Implement common adaptive algorithms such as LMS variants and RLS from algorithm tables suitable for direct coding.
- Analyze convergence, steady‑state error, and stability for adaptive filters under practical assumptions.
- Design and apply specialized schemes such as set‑membership, subband, and blind adaptive filtering to reduce complexity or work with colored inputs.
- Adapt and stabilize IIR adaptive filters and understand their pitfalls compared to FIR implementations.
- Evaluate and optimize fixed‑point and finite‑precision implementations for real DSP processors, accounting for complexity and numeric issues.
Topics Covered
- Introduction and basic concepts of adaptive filtering
- Mean‑square and stochastic performance analysis
- LMS family of algorithms and variants
- Recursive least squares (RLS) methods
- Set‑membership adaptive filtering
- Subband adaptive filtering
- Blind adaptive filtering and equalization
- Adaptive IIR filter structures and algorithms
- Implementation issues: complexity and fixed‑point effects
- Applications and case studies (echo cancellation, channel estimation, noise cancellation)
- Appendices: useful mathematical results and code references
Languages, Platforms & Tools
How It Compares
More implementation‑focused than Haykin's 'Adaptive Filter Theory' (which is more theory and derivation-heavy) and less formal/mathematical than Sayed's texts, making Diniz a good bridge between theory and practical coding.
