Adaptive System Identification and Signal Processing Algorithms (Prentice Hall International Series in Acoustics, Speech
An account of an important class of algorithmic families for adaptive system identification and signal processing. The LMS family and R&S and its fast versions, as well as the back propagation algorithms for neural networks, are examined in the context of algorithmic efficiency; that is, the issues of convergence, tracking, computational complexity, parallelism, roundoff error robustness and stability. The chapters are presented in such as way as to provide the basic derivation guidelines as well as the necessary performance analysis without sacrificing rigour. Typical applications areas such as channel equalization, echo concealment, spread spectrum RAKE receiver design and spectral analysis are presented and discussed.
Why Read This Book
You should read this book if you want a rigorous, algorithm-focused treatment of adaptive system identification that balances derivation, performance analysis, and implementation issues. It will show you how to choose and tune LMS/RLS-family methods, understand their convergence and tracking behavior, and handle numerical and complexity constraints in practical DSP systems.
Who Will Benefit
Practicing DSP engineers, communications engineers, and graduate students who design or analyze adaptive filters and adaptive signal-processing blocks for channel equalization, echo cancellation, RAKE receivers and spectral-analysis tasks.
Level: Advanced — Prerequisites: Solid linear algebra and probability/stochastic processes, fundamentals of signals and systems and basic DSP (z-transform, spectral concepts); familiarity with basic adaptive-filter concepts is helpful.
Key Takeaways
- Analyze the convergence and steady-state behavior of LMS and related stochastic-gradient algorithms.
- Implement and compare RLS and fast RLS algorithms, understanding tradeoffs between complexity and tracking.
- Assess numerical issues: finite-precision effects, roundoff robustness, and stability of adaptive algorithms.
- Design adaptive filters for practical applications such as channel equalization, echo cancellation and RAKE receivers.
- Evaluate algorithmic complexity and parallelism opportunities to guide efficient real-time implementations.
- Apply gradient-based learning perspectives (including backpropagation) within adaptive signal-processing frameworks.
Topics Covered
- Introduction and notation; overview of adaptive system identification
- Fundamentals of stochastic-gradient methods and LMS family
- Extended LMS variants and practical considerations
- Recursive least-squares (RLS) algorithms and derivations
- Fast RLS algorithms and complexity-reduction techniques
- Performance analysis: convergence, misadjustment, and tracking
- Numerical issues: finite precision, roundoff, and stability
- Parallel implementations and hardware-oriented considerations
- Gradient-based learning and backpropagation in signal processing
- Applications: channel equalization, echo cancellation, RAKE receivers
- Applications: spectral analysis and other use cases
- Implementation examples, simulations and concluding remarks
How It Compares
Covers similar adaptive-filter theory to Haykin's Adaptive Filter Theory but places more emphasis on algorithmic efficiency, numerical implementation and fast RLS variants; Widrow & Stearns is more historical/introductory while Haykin is broader and more modern in pedagogy.
