Adaptive Signal Processing : Next Generation Solutions

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Format: eBook
Pub. Date: 2010-06-01
Publisher(s): Wiley-IEEE Press
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Summary

Leading experts present the latest research results in adaptive signal processing Recent developments in signal processing have made it clear that significant performance gains can be achieved beyond those achievable using standard adaptive filtering approaches. Adaptive Signal Processing presents the next generation of algorithms that will produce these desired results, with an emphasis on important applications and theoretical advancements. This highly unique resource brings together leading authorities in the field writing on the key topics of significance, each at the cutting edge of its own area of specialty. It begins by addressing the problem of optimization in the complex domain, fully developing a framework that enables taking full advantage of the power of complex-valued processing. Then, the challenges of multichannel processing of complex-valued signals are explored. This comprehensive volume goes on to cover Turbo processing, tracking in the subspace domain, nonlinear sequential state estimation, and speech-bandwidth extension. Examines the seven most important topics in adaptive filtering that will define the next-generation adaptive filtering solutions Introduces the powerful adaptive signal processing methods developed within the last ten years to account for the characteristics of real-life data: non-Gaussianity, non-circularity, non-stationarity, and non-linearity Features self-contained chapters, numerous examples to clarify concepts, and end-of-chapter problems to reinforce understanding of the material Contains contributions from acknowledged leaders in the field Includes a Solutions Manual for instructors Adaptive Signal Processing is an invaluable tool for graduate students, researchers, and practitioners working in the areas of signal processing, communications, controls, radar, sonar, and biomedical engineering.

Table of Contents

Preface
Contributors
Complex-Valued Adaptive Signal Processing
Introduction
Preliminaries
Optimization in the Complex Domain
Widely Linear Adaptive Filtering
Nonlinear Adaptive Filtering with Multilayer Perceptrons
Complex Independent Component Analysis
Summary
Acknowledgment
Problems
References
Robust Estimation Techniques for Complex-Valued Random Vectors
Introduction
Statistical Characterization of Complex Random Vectors
Complex Elliptically Symmetric (CES) Distributions
Tools to Compare Estimators
Scatter and Pseudo-Scatter Matrices
Array Processing Examples
MVDR Beamformers Based on M-Estimators
Robust ICA
Conclusion
Problems
References
Turbo Equalization
Introduction
Context
Communication Chain
Turbo Decoder: Overview
Forward-Backward Algorithm
Simplified Algorithm: Interference Canceler
Capacity Analysis
Blind Turbo Equalization
Convergence
Multichannel and Multiuser Settings
Concluding Remarks
Problems
References
Subspace Tracking for Signal Processing
Introduction
Linear Algebra Review
Observation Model and Problem Statement
Preliminary Example: Oja's Neuron
Subspace Tracking
Eigenvectors Tracking
Convergence and Performance Analysis Issues
Illustrative Examples
Concluding Remarks
Problems
References
Particle Filtering
Introduction
Motivation for Use of Particle Filtering
The Basic Idea
The Choice of Proposal Distribution and Resampling
Some Particle Filtering Methods
Handling Constant Parameters
Rao-Blackwellization
Prediction
Smoothing
Convergence Issues
Computational Issues and Hardware Implementation
Acknowledgments
Exercises
References
Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems
Introduction
Back-Propagation and Support Vector Machine-Learning Algorithms: Review
Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation
The Extended Kalman Filter
Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms
Concluding Remarks
Problems
References
Bandwidth Extension of Telephony Speech
Introduction
Organization of the Chapter
Nonmodel-Based Algorithms for Bandwidth Extension
Basics
Model-Based Algorithms for Bandwidth Extension
Evaluation of Bandwidth Extension Algorithms
Conclusion
Problems
References
Index
Table of Contents provided by Publisher. All Rights Reserved.

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