Rent 9780470195178 | Adaptive Signal Processing : Next Generation Solutions | Adali, Tülay; Haykin, Simon @knetbooks.com

Summary Table of Contents Author Biography

Leading experts present the latest research results in adaptive signal processingRecent 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 o... MORE

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 Mappin... MOREgs	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 R^N	p. 31
Vector Optimization in C^N	p. 34
Matrix Optimization in C^N	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.

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.

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