Adaptive signal processing : next generation solutions

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Title:

Series:

Adaptive and learning systems for signal processing, communications, and control

Publication Information:

New York : IEEE, Institute of Electrical and Electronics Engineers ; [Hoboken, N.J.] : Wiley, 2010

Physical Description:

xv, 407 p. : ill. ; 24 cm.

ISBN:

9780470195178

Subject Term:

Adaptive signal processing

Added Author:

Adali, Tulay

Haykin, Simon S., 1931-

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010236595	TK5102.9 A33 2010	Open Access Book	Book	Searching... Unknown

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

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.

Author Notes

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.

Preface	p. xi
Contributors	p. xv
Chapter 1 Complex-Valued Adaptive Signal Processing	p. 1
1.1 Introduction	p. 1
1.1.1 Why Complex-Valued Signal Processing	p. 3
1.1.2 Outline of the Chapter	p. 5
1.2 Preliminaries	p. 6
1.2.1 Notation	p. 6
1.2.2 Efficient Computation of Derivatives in the Complex Domain	p. 9
1.2.3 Complex-to-Real and Complex-to-Complex Mappings	p. 17
1.2.4 Series Expansions	p. 20
1.2.5 Statistics of Complex-Valued Random Variables and Random Processes	p. 24
1.3 Optimization in the Complex Domain	p. 31
1.3.1 Basic Optimization Approaches in R N	p. 31
1.3.2 Vector Optimization in C N	p. 34
1.3.3 Matrix Optimization in C N	p. 37
1.3.4 Newton-Variant Updates	p. 38
1.4 Widely Linear Adaptive Filtering	p. 40
1.4.1 Linear and Widely Linear Mean-Square Error Filter	p. 41
1.5 Nonlinear Adaptive Filtering with Multilayer Perceptrons	p. 47
1.5.1 Choice of Activation Function for the MLP Filter	p. 48
1.5.2 Derivation of Back-Propagation Updates	p. 55
1.6 Complex Independent Component Analysis	p. 58
1.6.1 Complex Maximum Likelihood	p. 59
1.6.2 Complex Maximization of Non-Gaussianity	p. 64
1.6.3 Mutual Information Minimization: Connections to ML and MN	p. 66
1.6.4 Density Matching	p. 67
1.6.5 Numerical Examples	p. 71
1.7 Summary	p. 74
1.8 Acknowledgment	p. 76
1.9 Problems	p. 76
References	p. 79
Chapter 2 Robust Estimation Techniques for Complex-Valued Random Vectors	p. 87
2.1 Introduction	p. 87
2.1.1 Signal Model	p. 88
2.1.2 Outline of the Chapter	p. 90
2.2 Statistical Characterization of Complex Random Vectors	p. 91
2.2.1 Complex Random Variables	p. 91
2.2.2 Complex Random Vectors	p. 93
2.3 Complex Elliptically Symmetric (CES) Distributions	p. 95
2.3.1 Definition	p. 96
2.3.2 Circular Case	p. 98
2.3.3 Testing the Circularity Assumption	p. 99
2.4 Tools to Compare Estimators	p. 102
2.4.1 Robustness and Influence Function	p. 102
2.4.2 Asymptotic Performance of an Estimator	p. 106
2.5 Scatter and Pseudo-Scatter Matrices	p. 107
2.5.1 Background and Motivation	p. 107
2.5.2 Definition	p. 108
2.5.3 M-Estimators of Scatter	p. 110
2.6 Array Processing Examples	p. 114
2.6.1 Beamformers	p. 114
2.6.2 Subspace Methods	p. 115
2.6.3 Estimating the Number of Sources	p. 118
2.6.4 Subspace DOA Estimation for Noncircular Sources	p. 120
2.7 MVDR Beamformers Based on M-Estimators	p. 121
2.7.1 The Influence Function Study	p. 123
2.8 Robust ICA	p. 128
2.8.1 The Class of DOGMA Estimators	p. 129
2.8.2 The Class of GUT Estimators	p. 132
2.8.3 Communications Example	p. 134
2.9 Conclusion	p. 137
2.10 Problems	p. 137
References	p. 138
Chapter 3 Turbo Equalization	p. 143
3.1 Introduction	p. 143
3.2 Context	p. 144
3.3 Communication Chain	p. 145
3.4 Turbo Decoder: Overview	p. 147
3.4.1 Basic Properties of Iterative Decoding	p. 151
3.5 Forward-Backward Algorithm	p. 152
3.5.1 With Intersymbol Interference	p. 160
3.6 Simplified Algorithm: Interference Canceler	p. 163
3.7 Capacity Analysis	p. 168
3.8 Blind Turbo Equalization	p. 173
3.8.1 Differential Encoding	p. 179
3.9 Convergence	p. 182
3.9.1 Bit Error Probability	p. 187
3.9.2 Other Encoder Variants	p. 190
3.9.3 EXIT Chart for Interference Canceler	p. 192
3.9.4 Related Analyses	p. 194
3.10 Multichannel and Multiuser Settings	p. 195
3.10.1 Forward-Backward Equalizer	p. 196
3.10.2 Interference Canceler	p. 197
3.10.3 Multiuser Case	p. 198
3.11 Concluding Remarks	p. 199
3.12 Problems	p. 200
References	p. 206
Chapter 4 Subspace Tracking for Signal Processing	p. 211
4.1 Introduction	p. 211
4.2 Linear Algebra Review	p. 213
4.2.1 Eigenvalue Value Decomposition	p. 213
4.2.2 QR Factorization	p. 214
4.2.3 Variational Characterization of Eigenvalues/Eigenvectors of Real Symmetric Matrices	p. 215
4.2.4 Standard Subspace Iterative Computational Techniques	p. 216
4.2.5 Characterization of the Principal Subspace of a Covariance Matrix from the Minimization of a Mean Square Error	p. 218
4.3 Observation Model and Problem Statement	p. 219
4.3.1 Observation Model	p. 219
4.3.2 Statement of the Problem	p. 220
4.4 Preliminary Example: Oja's Neuron	p. 221
4.5 Subspace Tracking	p. 223
4.5.1 Subspace Power-Based Methods	p. 224
4.5.2 Projection Approximation-Based Methods	p. 230
4.5.3 Additional Methodologies	p. 232
4.6 Eigenvectors Tracking	p. 233
4.6.1 Rayleigh Quotient-Based Methods	p. 234
4.6.2 Eigenvector Power-Based Methods	p. 235
4.6.3 Projection Approximation-Based Methods	p. 240
4.6.4 Additional Methodologies	p. 240
4.6.5 Particular Case of Second-Order Stationary Data	p. 242
4.7 Convergence and Performance Analysis Issues	p. 243
4.7.1 A Short Review of the ODE Method	p. 244
4.7.2 A Short Review of a General Gaussian Approximation Result	p. 246
4.7.3 Examples of Convergence and Performance Analysis	p. 248
4.8 Illustrative Examples	p. 256
4.8.1 Direction of Arrival Tracking	p. 257
4.8.2 Blind Channel Estimation and Equalization	p. 258
4.9 Concluding Remarks	p. 260
4.10 Problems	p. 260
References	p. 266
Chapter 5 Particle Filtering	p. 271
5.1 Introduction	p. 272
5.2 Motivation for Use of Particle Filtering	p. 274
5.3 The Basic Idea	p. 278
5.4 The Choice of Proposal Distribution and Resampling	p. 289
5.4.1 Choice of Proposal Distribution	p. 290
5.4.2 Resampling	p. 291
5.5 Some Particle Filtering Methods	p. 295
5.5.1 SIR Particle Filtering	p. 295
5.5.2 Auxiliary Particle Filtering	p. 297
5.5.3 Gaussian Particle Filtering	p. 301
5.5.4 Comparison of the Methods	p. 302
5.6 Handling Constant Parameters	p. 305
5.6.1 Kernel-Based Auxiliary Particle Filter	p. 306
5.6.2 Density-Assisted Particle Filter	p. 308
5.7 Rao-Blackwellization	p. 310
5.8 Prediction	p. 314
5.9 Smoothing	p. 316
5.10 Convergence Issues	p. 320
5.11 Computational Issues and Hardware Implementation	p. 323
5.12 Acknowledgments	p. 324
5.13 Exercises	p. 325
References	p. 327
Chapter 6 Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems	p. 333
6.1 Introduction	p. 333
6.2 Back-Propagation and Support Vector Machine-Learning Algorithms: Review	p. 334
6.2.1 Back-Propagation Learning	p. 334
6.2.2 Support Vector Machine	p. 337
6.3 Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation	p. 340
6.4 The Extended Kalman Filter	p. 341
6.4.1 The EKF Algorithm	p. 344
6.5 Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms	p. 344
6.6 Concluding Remarks	p. 347
6.7 Problems	p. 348
References	p. 348
Chapter 7 Bandwidth Extension of Telephony Speech	p. 349
7.1 Introduction	p. 349
7.2 Organization of the Chapter	p. 352
7.3 Nonmodel-Based Algorithms for Bandwidth Extension	p. 352
7.3.1 Oversampling with Imaging	p. 353
7.3.2 Application of Nonlinear Characteristics	p. 353
7.4 Basics	p. 354
7.4.1 Source-Filter Model	p. 355
7.4.2 Parametric Representations of the Spectral Envelope	p. 358
7.4.3 Distance Measures	p. 362
7.5 Model-Based Algorithms for Bandwidth Extension	p. 364
7.5.1 Generation of the Excitation Signal	p. 365
7.5.2 Vocal Tract Transfer Function Estimation	p. 369
7.6 Evaluation of Bandwidth Extension Algorithms	p. 383
7.6.1 Objective Distance Measures	p. 383
7.6.2 Subjective Distance Measures	p. 385
7.7 Conclusion	p. 388
7.8 Problems	p. 388
References	p. 390
Index	p. 393

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Table of Contents