ISBN 9788126535675,Adaptive Signal Processing: Next-Generation Solutions

Adaptive Signal Processing: Next-Generation Solutions

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ISBN 9788126535675
Publisher

Wiley India Pvt Ltd

Publication Year 2012
ISBN-13

ISBN 9788126535675

ISBN-10 8126535679
Binding

Paperback

Number of Pages 424 Pages
Language (English)
Subject

Engineering & Technology

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

Key Features:
Book emphasizes important applications and theoretical advances, eg, complex-valued signal processing
Book examines the seven most important topics in adaptive filtering that will define the next generation adaptive filtering solutions
All contributors are acknowledged leaders in the subjects of their contributions
Book have end of chapter problems

About the Author
Tulay Adali PhD, is Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County. Professor Adali, earned his PhD in Electrical and Computer Engineering at North Carolina State University in 1992. Dr. Adali's research interests are in statistical signal processing, neural computation, adaptive signal processing, biomedical data analysis, ormatics and communications. Simon Haykin, PhD, is Professor and Director of Neurocomputation for Signal Processing at McMaster University.

Simon Haykin is a noted authority on adaptive and learning systems. He has pioneered signal-processing techniques and systems for radar and communication applications, and authored several fundamental textbooks in those fields. From 1972 to 1993, he served as founding director of McMaster's Communications Research Laboratory. Continually developing new curricula, Dr. Haykin has created innovative courses in emerging fields: neural networks, Bayesian sequential state estimation and space-time communication theory. An IEEE Life Fellow and a Fellow of The Royal Society of Canada, Dr. Haykin has received the IEEE Signal Processing Society Education Award, the IEEE Education Society McGraw-Hill/Jacob Millman Award, the IEEE Region 7 McNaughton Gold Medal and the International Union of Radio Science's Booker Gold Medal.

Table of Contents
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
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Table of Contents
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.
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