Adaptive Filter Theory

CONTENTS

Preface
Acknowledgments

Background and Preview

• Chapter 1 Stochastic Processes and Models
• Chapter 2 Wiener Filters
• Chapter 3 Linear Prediction
• Chapter 4 Method of Steepest Descent
• Chapter 5 Least-Mean-Square Adaptive Filters
• Chapter 6 Normalized Least-Mean-Square Adaptive Filters
• Chapter 7 Frequency-Domain and Subband Adaptive Filters
• Chapter 8 Method of Least Squares
• Chapter 9 Recursive Least-Square Adaptive Filters
• Chapter 10 Kalman Filters
• Chapter 11 Square-Root Adaptive Filters
• Chapter 12 Order-Recursive Adaptive Filters
• Chapter 13 Finite-Precision Effects
• Chapter 14 Tracking of Time-Varying Systems
• Chapter 15 Adaptive Filters Using Infinite-Duration Impulse Response Structures
• Chapter 16 Blind Deconvolution
• Chapter 17 Back-Propagation Learning

Epilogue

• Appendix A Complex Variables
• Appendix B Differentiation with Respect to a Vector
• Appendix C Method of Lagrange Multipliers
• Appendix D Estimation Theory
• Appendix E Eigenanalysis
• Appendix F Rotations and Reflections
• Appendix G Complex Wishart Distribution
• Glossary
• Bibliography
• Index

Adaptive Filter Theory looks at both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. Up-to-date and in-depth treatment of adaptive filters develops concepts in a unified and accessible manner. This highly successful book provides comprehensive coverage of adaptive filters in a highly readable and understandable fashion. Includes an extensive use of illustrative examples; and MATLAB experiments, which illustrate the practical realities and intricacies of adaptive filters, the codes for which can be downloaded from the Web. Covers a wide range of topics including Stochastic Processes, Wiener Filters, and Kalman Filters. For those interested in learning about adaptive filters and the theories behind them.

Includes bibliographical references (p. 941-977) and index.

Background and Overview.

 1. Stochastic Processes and Models.

 2. Wiener Filters.

 3. Linear Prediction.

 4. Method of Steepest Descent.

 5. Least-Mean-Square Adaptive Filters.

 6. Normalized Least-Mean-Square Adaptive Filters.

 7. Transform-Domain and Sub-Band Adaptive Filters.

 8. Method of Least Squares.

 9. Recursive Least-Square Adaptive Filters.

10. Kalman Filters as the Unifying Bases for RLS Filters.

11. Square-Root Adaptive Filters.

12. Order-Recursive Adaptive Filters.

13. Finite-Precision Effects.

14. Tracking of Time-Varying Systems.

15. Adaptive Filters Using Infinite-Duration Impulse Response Structures.

16. Blind Deconvolution.

17. Back-Propagation Learning.

Epilogue.

Appendix A. Complex Variables.

Appendix B. Differentiation with Respect to a Vector.

Appendix C. Method of Lagrange Multipliers.

Appendix D. Estimation Theory.

Appendix E. Eigenanalysis.

Appendix F. Rotations and Reflections.

Appendix G. Complex Wishart Distribution.

Glossary.

Abbreviations.

Principal Symbols.

Bibliography.

Index.