Adaptive Filtering Primer with MATLAB (Electrical Engineering Primer Series)
Because of the wide use of adaptive filtering in digital signal processing and, because most of the modern electronic devices include some type of an adaptive filter, a text that brings forth the fundamentals of this field was necessary. The material and the principles presented in this book are easily accessible to engineers, scientists, and students who would like to learn the fundamentals of this field and have a background at the bachelor level.
Adaptive Filtering Primer with MATLAB® clearly explains the fundamentals of adaptive filtering supported by numerous examples and computer simulations. The authors introduce discrete-time signal processing, random variables and stochastic processes, the Wiener filter, properties of the error surface, the steepest descent method, and the least mean square (LMS) algorithm. They also supply many MATLAB® functions and m-files along with computer experiments to illustrate how to apply the concepts to real-world problems. The book includes problems along with hints, suggestions, and solutions for solving them. An appendix on matrix computations completes the self-contained coverage.
With applications across a wide range of areas, including radar, communications, control, medical instrumentation, and seismology, Adaptive Filtering Primer with MATLAB® is an ideal companion for quick reference and a perfect, concise introduction to the field.
Why Read This Book
You should read this book if you want a compact, application-minded introduction to adaptive filters that balances theory and hands-on MATLAB examples. It helps you move quickly from fundamental concepts (Wiener filters, error surfaces) to implementing LMS/NLMS/RLS and evaluating their performance in realistic scenarios.
Who Will Benefit
Undergraduate or graduate students and practicing DSP engineers who need a practical, MATLAB-driven grounding in adaptive filtering for tasks like noise cancellation, system identification, or channel equalization.
Level: Intermediate — Prerequisites: Basic signals and systems (discrete-time), probability and random processes fundamentals, and familiarity with MATLAB for running and modifying provided examples.
Key Takeaways
- Derive and implement the Wiener solution for mean-square optimal linear estimation.
- Implement and tune LMS and normalized LMS (NLMS) adaptive algorithms in MATLAB.
- Implement and evaluate RLS and other fast-converging adaptive algorithms.
- Analyze convergence behavior and steady-state mean-square error of adaptive algorithms.
- Apply adaptive filters to practical problems such as noise cancellation, system identification, and echo/channel equalization.
Topics Covered
- Introduction and motivation for adaptive filtering
- Review of discrete-time signals and random processes
- Optimal linear estimation and the Wiener filter
- Error surface properties and steepest-descent methods
- The LMS algorithm: derivation, stability, and implementation
- Normalized and transform-domain LMS variants
- Recursive Least Squares (RLS) and fast algorithms
- Performance analysis: convergence, misadjustment, and steady-state MSE
- Practical issues: tracking, regularization, and numerical aspects
- Applications: noise cancellation, system identification, echo cancellation
- MATLAB examples and simulation recipes
- Exercises and further reading
Languages, Platforms & Tools
How It Compares
More accessible and MATLAB-focused than Haykin's Adaptive Filter Theory (which is more theoretical and comprehensive); more modern and practical than older texts like Widrow & Stearns for hands-on simulation work.
