第五版《自适应滤波理论》：现代通信研究基石

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《自适应滤波理论第五版》（Adaptive Filter Theory 5th）是由Simon Haykin撰写，由McMaster University的Communications Research Laboratory出版的一部权威著作。本书是针对信号处理和通信工程领域的经典教材，第五版更新了内容，旨在提供非扫描的高清PDF格式，以满足现代学习者的需求。作者Simon Haykin以其在该领域的深厚学术背景，系统地阐述了自适应滤波器的基本原理、设计方法、应用策略以及其在通信系统、噪声抑制、信号估计等方面的关键作用。本书涵盖了以下几个核心知识点： 1. **自适应滤波基础**：首先，读者将学习自适应滤波器的概念，包括线性滤波器、无限 impulse response (IIR) 滤波器和有限 impulse response (FIR) 滤波器的区别，以及它们如何通过最小均方误差（Minimum Mean Square Error, MMSE）或岭回归等准则进行优化。 2. **自适应算法**：书中深入剖析了常用的自适应算法，如 Least Mean Squares (LMS), Recursive Least Squares (RLS), Lattice Filter等，以及它们的收敛性、稳定性和性能特点，同时讨论了算法的改进和扩展。 3. **应用实例**：书中提供了丰富的实际应用案例，如信号分离、噪声抑制、系统识别、无线通信中的均衡和干扰抵消，帮助读者理解和掌握这些技术在实际问题中的解决方案。 4. **实时性和硬件实现**：第五版还可能包含了对自适应滤波器在嵌入式系统和实时环境下的考虑，包括硬件资源限制下的算法优化和性能分析。 5. **最新进展与趋势**：作为第五版，书中会关注近年来自适应滤波技术的最新研究成果，如多尺度滤波、深度学习在自适应滤波中的应用，以及与大数据、机器学习相结合的可能性。 6. **理论与实践结合**：作者以理论与实践并重的方式，确保读者不仅能理解理论概念，还能掌握如何在实际项目中实施和优化自适应滤波器。《自适应滤波理论第五版》是一本既适合研究生和科研人员进行深入研究，也适合工程师和技术人员提升技能的专业书籍，对于任何想要深入了解这一领域的读者来说，都是一份不可或缺的参考资料。通过阅读这本书，读者能够系统地了解自适应滤波器的理论与应用，并将其应用于解决复杂信号处理问题。

• Help the reader to develop a deeper understanding of the material covered in the

chapter.

• Challenge the reader to extend some aspects of the theory discussed in the

chapter.

Solutions Manual

The book has a companion solutions manual that presents detailed solutions to all the

problems at the end of Chapters 1 through 17 of the book. A copy of the manual can

be obtained by instructors who have adopted the book for classroom use by writing

directly to the publisher.

The MATLAB codes for all the computer experiments can be accessed by going

to the web site http://www.pearsonhighered.com/haykin/.

Two Noteworthy Symbols

Typically, the square-root of minus one is denoted by the italic symbol j, and the dif-

ferential operator (used in differentiation as well as integration) is denoted by the italic

symbol d. In reality, however, both of these terms are operators, each in its own way; it

is therefore incorrect to use italic symbols for their notations. Furthermore, the italic

symbol j and the italic symbol d are also frequently used as indices to represent other

matters, thereby raising the potential for confusion. Accordingly, throughout the book,

the roman symbol j and the roman symbol d are used to denote the square root of minus

one and the differential operator, respectively.

Use of the Book

The book is written at a level suitable for use in graduate courses on adaptive signal

processing. In this context, it is noteworthy that the organization of the material covered

in the book offers a great deal of flexibility in the selection of a suitable list of topics for

such a graduate course.

It is hoped that the book will also be useful to researchers and engineers in indus-

try as well as government establishments, working on problems relating to the theory

and applications of adaptive filters.

Simon Haykin

Ancaster, Ontario,

Canada

Preface xv

xvi

I would like to thank several colleagues who helped me in one form or another while

writing this book:

1. Prof. Babak Hassibi, Department of Electrical Engineering, California Institute

of Technology (Caltech), Pasadena, California, for reading through Chapter 11 on

robustness and for numerous comments and suggestions.

2. Mr. Ashique Rupam Mahmood, Department of Computing Science, University

of Alberta, Edmonton, Alberta, Canada, for teaching me the merits of his newly

developed procedure, the Autostep method, to train an adaptive filtering algo-

rithm with no manual tuning and for contributing experiments as well as end-of-

chapter problems in Chapter 13.

3. Prof. Jose Principe, Department of Electrical and Computer Engineering,

University of Florida, Gainesville, Florida, for his many contributions to the

kernelleast-mean-square (LMS) algorithm in the Epilogue.

4. Prof. Phillip Regalia, Department of Electrical Engineering and Computer Science,

Catholic University of America, Washington, D.C., for his continued interest in

adaptive filtering and for sharpening Problem 10 in Chapter 16 on order-recursive

least-squares lattice filtering.

5. Prof. Richard Sutton, Department of Computing Science, University of Alberta,

Edmonton, Alberta, Canada, for introducing me to his novel incremental delta-

bar-delta (IDBD) algorithm.

6. Dr. Peyman Setoodeh, Department of Electrical and Computer Engineering,

McMaster University, Hamilton, Ontario, Canada, for taking care of some new

computer experiments in Chapter 6 on the LMS algorithm.

7. Prof. Sergios Theodoridis, Department of Informatics and Telecommunications,

University of Athens, Athens, Greece, for many constructive comments on the

attributes and limitations of the kernel LMS algorithm in the Epilogue.

8. Dr. Weifeng Liu, formerly at the University of Florida and presently at the financial

company Jump Trading, LLC, Chicago, Illinois, for many comments on the kernel

LMS algorithm in the Epilogue and for contributing to the computer experiment

therein.

I am indebted to all of them for enriching the book through their individual contributions.

Acknowledgments

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