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A Rapid Introduction to Adaptive Filtering

  • Book
  • © 2013

Overview

Authors:
  1. Leonardo Rey Vega

    1. , School of Engineering, University of Buenos Aires, Buenos Aires, Argentina

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    You can also search for this author in PubMed   Google Scholar

  2. Hernan Rey

    1. , Department of Engineering, University of Leicester, Leicester, United Kingdom

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    You can also search for this author in PubMed   Google Scholar

  • Presents a rapid introduction to the fundamentals on adaptive filtering
  • Discusses several modern topics in the adaptive filtering field
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xii
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  2. Introduction

    • Leonardo Rey Vega, Hernan Rey
    Pages 1-5

  3. Wiener Filtering

    • Leonardo Rey Vega, Hernan Rey
    Pages 7-17

  4. Iterative Optimization

    • Leonardo Rey Vega, Hernan Rey
    Pages 19-31

  5. Stochastic Gradient Adaptive Algorithms

    • Leonardo Rey Vega, Hernan Rey
    Pages 33-88

  6. Least Squares

    • Leonardo Rey Vega, Hernan Rey
    Pages 89-112

  7. Advanced Topics and New Directions

    • Leonardo Rey Vega, Hernan Rey
    Pages 113-119

  8. Back Matter

    Pages 121-122
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Keywords

About this book

In this book, the authors provide insights into the basics of adaptive filtering, which are particularly useful for students taking their first steps into this field. They start by studying the problem of minimum mean-square-error filtering, i.e., Wiener filtering. Then, they analyze iterative methods for solving the optimization problem, e.g., the Method of Steepest Descent. By proposing stochastic approximations, several basic adaptive algorithms are derived, including Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS) and Sign-error algorithms. The authors provide a general framework to study the stability and steady-state performance of these algorithms. The affine Projection Algorithm (APA) which provides faster convergence at the expense of computational complexity (although fast implementations can be used) is also presented. In addition, the Least Squares (LS) method and its recursive version (RLS), including fast implementations are discussed. The book closes withthe discussion of several topics of interest in the adaptive filtering field.

Reviews

From the reviews:

“Digital signal processing (DSP) is a popular course for undergraduate students in electrical and communications engineering. … This book can be read in a few days. The book has six chapters, including a chapter each for an introduction and advanced topics. The presentation is easy to read and understandable, and the authors provide both theoretical and mathematical treatments of the material. … The book could also serve well as a quick reference for engineers and students who are developing DSP solutions.” (S. Ramakrishnan, ACM Computing Reviews, December, 2012)

Authors and Affiliations

  • , School of Engineering, University of Buenos Aires, Buenos Aires, Argentina

    Leonardo Rey Vega

  • , Department of Engineering, University of Leicester, Leicester, United Kingdom

    Hernan Rey

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