Adaptive Signal Processing Next Generation Solutions
by Adali, Tülay; Haykin, SimonEdition: 1st
Format: Hardcover
Pub. Date: 2010-03-15
Publisher(s): Wiley-IEEE Press
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Summary
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.
Author Biography
Tulay Adali, PhD, is Professor of Electrical Engineering and Director of the Machine Learning for Signal Processing Laboratory at the University of Maryland, Baltimore County. Her research interests are in statistical and adaptive signal processing, with emphasis on nonlinear and complex-valued signal processing, and applications in biomedical data analysis and communications. Simon Haykin PhD, is Distinguished University Professor and Director of the Cognitive Systems Laboratory in the Faculty of Engineering at McMaster University. A world-renowned authority on adaptive and learning systems, Dr. Haykin has pioneered signal-processing techniques and systems for radar and communication applications, culminating in the study of cognitive dynamic systems, which has become his research passion.
Table of Contents
| Preface | p. xi |
| Contributors | p. xv |
| Complex-Valued Adaptive Signal Processing | p. 1 |
| Introduction | p. 1 |
| Why Complex-Valued Signal Processing | p. 3 |
| Outline of the Chapter | p. 5 |
| Preliminaries | p. 6 |
| Notation | p. 6 |
| Efficient Computation of Derivatives in the Complex Domain | p. 9 |
| Complex-to-Real and Complex-to-Complex Mappings | p. 17 |
| Series Expansions | p. 20 |
| Statistics of Complex-Valued Random Variables and Random Processes | p. 24 |
| Optimization in the Complex Domain | p. 31 |
| Basic Optimization Approaches in RN | p. 31 |
| Vector Optimization in CN | p. 34 |
| Matrix Optimization in CN | p. 37 |
| Newton-Variant Updates | p. 38 |
| Widely Linear Adaptive Filtering | p. 40 |
| Linear and Widely Linear Mean-Square Error Filter | p. 41 |
| Nonlinear Adaptive Filtering with Multilayer Perceptrons | p. 47 |
| Choice of Activation Function for the MLP Filter | p. 48 |
| Derivation of Back-Propagation Updates | p. 55 |
| Complex Independent Component Analysis | p. 58 |
| Complex Maximum Likelihood | p. 59 |
| Complex Maximization of Non-Gaussianity | p. 64 |
| Mutual Information Minimization: Connections to ML and MN | p. 66 |
| Density Matching | p. 67 |
| Numerical Examples | p. 71 |
| Summary | p. 74 |
| Acknowledgment | p. 76 |
| Problems | p. 76 |
| References | p. 79 |
| Robust Estimation Techniques for Complex-Valued Random Vectors | p. 87 |
| Introduction | p. 87 |
| Signal Model | p. 88 |
| Outline of the Chapter | p. 90 |
| Statistical Characterization of Complex Random Vectors | p. 91 |
| Complex Random Variables | p. 91 |
| Complex Random Vectors | p. 93 |
| Complex Elliptically Symmetric (CES) Distributions | p. 95 |
| Definition | p. 96 |
| Circular Case | p. 98 |
| Testing the Circularity Assumption | p. 99 |
| Tools to Compare Estimators | p. 102 |
| Robustness and Influence Function | p. 102 |
| Asymptotic Performance of an Estimator | p. 106 |
| Scatter and Pseudo-Scatter Matrices | p. 107 |
| Background and Motivation | p. 107 |
| Definition | p. 108 |
| M-Estimators of Scatter | p. 110 |
| Array Processing Examples | p. 114 |
| Beamformers | p. 114 |
| Subspace Methods | p. 115 |
| Estimating the Number of Sources | p. 118 |
| Subspace DOA Estimation for Noncircular Sources | p. 120 |
| MVDR Beamformers Based on M-Estimators | p. 121 |
| The Influence Function Study | p. 123 |
| Robust ICA | p. 128 |
| The Class of DOGMA Estimators | p. 129 |
| The Class of GUT Estimators | p. 132 |
| Communications Example | p. 134 |
| Conclusion | p. 137 |
| Problems | p. 137 |
| References | p. 138 |
| Turbo Equalization | p. 143 |
| Introduction | p. 143 |
| Context | p. 144 |
| Communication Chain | p. 145 |
| Turbo Decoder: Overview | p. 147 |
| Basic Properties of Iterative Decoding | p. 151 |
| Forward-Backward Algorithm | p. 152 |
| With Intersymbol Interference | p. 160 |
| Simplified Algorithm: Interference Canceler | p. 163 |
| Capacity Analysis | p. 168 |
| Blind Turbo Equalization | p. 173 |
| Differential Encoding | p. 179 |
| Convergence | p. 182 |
| Bit Error Probability | p. 187 |
| Other Encoder Variants | p. 190 |
| EXIT Chart for Interference Canceler | p. 192 |
| Related Analyses | p. 194 |
| Multichannel and Multiuser Settings | p. 195 |
| Forward-Backward Equalizer | p. 196 |
| Interference Canceler | p. 197 |
| Multiuser Case | p. 198 |
| Concluding Remarks | p. 199 |
| Problems | p. 200 |
| References | p. 206 |
| Subspace Tracking for Signal Processing | p. 211 |
| Introduction | p. 211 |
| Linear Algebra Review | p. 213 |
| Eigenvalue Value Decomposition | p. 213 |
| QR Factorization | p. 214 |
| Variational Characterization of Eigenvalues/Eigenvectors of Real Symmetric Matrices | p. 215 |
| Standard Subspace Iterative Computational Techniques | p. 216 |
| Characterization of the Principal Subspace of a Covariance Matrix from the Minimization of a Mean Square Error | p. 218 |
| Observation Model and Problem Statement | p. 219 |
| Observation Model | p. 219 |
| Statement of the Problem | p. 220 |
| Preliminary Example: Oja's Neuron | p. 221 |
| Subspace Tracking | p. 223 |
| Subspace Power-Based Methods | p. 224 |
| Projection Approximation-Based Methods | p. 230 |
| Additional Methodologies | p. 232 |
| Eigenvectors Tracking | p. 233 |
| Rayleigh Quotient-Based Methods | p. 234 |
| Eigenvector Power-Based Methods | p. 235 |
| Projection Approximation-Based Methods | p. 240 |
| Additional Methodologies | p. 240 |
| Particular Case of Second-Order Stationary Data | p. 242 |
| Convergence and Performance Analysis Issues | p. 243 |
| A Short Review of the ODE Method | p. 244 |
| A Short Review of a General Gaussian Approximation Result | p. 246 |
| Examples of Convergence and Performance Analysis | p. 248 |
| Illustrative Examples | p. 256 |
| Direction of Arrival Tracking | p. 257 |
| Blind Channel Estimation and Equalization | p. 258 |
| Concluding Remarks | p. 260 |
| Problems | p. 260 |
| References | p. 266 |
| Particle Filtering | p. 271 |
| Introduction | p. 272 |
| Motivation for Use of Particle Filtering | p. 274 |
| The Basic Idea | p. 278 |
| The Choice of Proposal Distribution and Resampling | p. 289 |
| Choice of Proposal Distribution | p. 290 |
| Resampling | p. 291 |
| Some Particle Filtering Methods | p. 295 |
| SIR Particle Filtering | p. 295 |
| Auxiliary Particle Filtering | p. 297 |
| Gaussian Particle Filtering | p. 301 |
| Comparison of the Methods | p. 302 |
| Handling Constant Parameters | p. 305 |
| Kernel-Based Auxiliary Particle Filter | p. 306 |
| Density-Assisted Particle Filter | p. 308 |
| Rao-Blackwellization | p. 310 |
| Prediction | p. 314 |
| Smoothing | p. 316 |
| Convergence Issues | p. 320 |
| Computational Issues and Hardware Implementation | p. 323 |
| Acknowledgments | p. 324 |
| Exercises | p. 325 |
| References | p. 327 |
| Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems | p. 333 |
| Introduction | p. 333 |
| Back-Propagation and Support Vector Machine-Learning Algorithms: Review | p. 334 |
| Back-Propagation Learning | p. 334 |
| Support Vector Machine | p. 337 |
| Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation | p. 340 |
| The Extended Kalman Filter | p. 341 |
| The EKF Algorithm | p. 344 |
| Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms | p. 344 |
| Concluding Remarks | p. 347 |
| Problems | p. 348 |
| References | p. 348 |
| Bandwidth Extension of Telephony Speech | p. 349 |
| Introduction | p. 349 |
| Organization of the Chapter | p. 352 |
| Nonmodel-Based Algorithms for Bandwidth Extension | p. 352 |
| Oversampling with Imaging | p. 353 |
| Application of Nonlinear Characteristics | p. 353 |
| Basics | p. 354 |
| Source-Filter Model | p. 355 |
| Parametric Representations of the Spectral Envelope | p. 358 |
| Distance Measures | p. 362 |
| Model-Based Algorithms for Bandwidth Extension | p. 364 |
| Generation of the Excitation Signal | p. 365 |
| Vocal Tract Transfer Function Estimation | p. 369 |
| Evaluation of Bandwidth Extension Algorithms | p. 383 |
| Objective Distance Measures | p. 383 |
| Subjective Distance Measures | p. 385 |
| Conclusion | p. 388 |
| Problems | p. 388 |
| References | p. 390 |
| Index | p. 393 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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