1. Auflage - April 2010
109,- Euro
2010. 408 Seiten, Hardcover
ISBN-10: 0-470-19517-7
ISBN-13: 978-0-470-19517-8 - John Wiley & Sons

Preis inkl. Mehrwertsteuer zzgl. Versandkosten.





Probekapitel

Kurzbeschreibung
This book presents the latest research results in adaptive signal processing with an emphasis on important applications and theoretical advancements. Each chapter is self-contained, comprehensive in its coverage, and written by a leader in his or her field of specialty. A uniform style is maintained throughout the book and each chapter concludes with problems for readers to reinforce their understanding of the material presented. The book can be used as a reliable reference for researchers and practitioners or as a textbook for graduate students.

Aus dem Inhalt
Preface.

Contributors.

Chapter 1 Complex-Valued Adaptive Signal Processing.

1.1 Introduction.


1.2 Preliminaries.

1.3 Optimization in the Complex Domain.

1.4 Widely Linear Adaptive Filtering.

1.5 Nonlinear Adaptive Filtering with Multilayer Perceptrons.

1.6 Complex Independent Component Analysis.

1.7 Summary.

1.8 Acknowledgment.

1.9 Problems.

References.

Chapter 2 Robust Estimation Techniques for Complex-Valued Random Vectors.

2.1 Introduction.

2.2 Statistical Characterization of Complex Random Vectors.

2.3 Complex Elliptically Symmetric (CES) Distributions.

2.4 Tools to Compare Estimators.

2.5 Scatter and Pseudo-Scatter Matrices.

2.6 Array Processing Examples.

2.7 MVDR Beamformers Based on M-Estimators.

2.8 Robust ICA.

2.9 Conclusion.

2.10 Problems.

References.

Chapter 3 Turbo Equalization.

3.1 Introduction.

3.2 Context.

3.3 Communication Chain.

3.4 Turbo Decoder: Overview.

3.5 Forward-Backward Algorithm.

3.6 Simplified Algorithm: Interference Canceler.

3.7 Capacity Analysis.

3.8 Blind Turbo Equalization.

3.9 Convergence.

3.10 Multichannel and Multiuser Settings.

3.11 Concluding Remarks.

3.12 Problems.

References.

Chapter 4 Subspace Tracking for Signal Processing.

4.1 Introduction.

4.2 Linear Algebra Review.

4.3 Observation Model and Problem Statement.

4.4 Preliminary Example: Oja's Neuron.

4.5 Subspace Tracking.

4.6 Eigenvectors Tracking.

4.7 Convergence and Performance Analysis Issues.

4.8 Illustrative Examples.

4.9 Concluding Remarks.

4.10 Problems.

References.

Chapter 5 Particle Filtering.

5.1 Introduction.

5.2 Motivation for Use of Particle Filtering.

5.3 The Basic Idea.

5.4 The Choice of Proposal Distribution and Resampling.

5.5 Some Particle Filtering Methods.

5.6 Handling Constant Parameters.

5.7 Rao-Blackwellization.

5.8 Prediction.

5.9 Smoothing.

5.10 Convergence Issues.

5.11 Computational Issues and Hardware Implementation.

5.12 Acknowledgments.

5.13 Exercises.

References.

Chapter 6 Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems.

6.1 Introduction.

6.2 Back-Propagation and Support Vector Machine-Learning Algorithms: Review.

6.3 Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation.

6.4 The Extended Kalman Filter.

6.5 Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms.

6.6 Concluding Remarks.

6.7 Problems.

References.

Chapter 7 Bandwidth Extension of Telephony Speech.

7.1 Introduction.

7.2 Organization of the Chapter.

7.3 Nonmodel-Based Algorithms for Bandwidth Extension.

7.4 Basics.

7.5 Model-Based Algorithms for Bandwidth Extension.

7.6 Evaluation of Bandwidth Extension Algorithms.

7.7 Conclusion.

7.8 Problems.

References.

Index.