Adaptive Signal Processing – Next Generation ons

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. Contributors. Chapter 1 Complex-Valued Adaptive Signal Processing. 1.1 Introduction. 1.2 Preliminaries. 1.3 Optimization in the Complex Domain. 1.4 Widely Linear Adaptive Filtering. 1.5 Nonlinear Adaptive Filtering with Multilayer Perceptrons. 1.6 Complex Independent Component Analysis. 1.7 Summary. 1.8 Acknowledgment. 1.9 Problems. References. Chapter 2 Robust Estimation Techniques for Complex-Valued Random Vectors. 2.1 Introduction. 2.2 Statistical Characterization of Complex Random Vectors. 2.3 Complex Elliptically Symmetric (CES) Distributions. 2.4 Tools to Compare Estimators. 2.5 Scatter and Pseudo-Scatter Matrices. 2.6 Array Processing Examples. 2.7 MVDR Beamformers Based on M -Estimators. 2.8 Robust ICA. 2.9 Conclusion. 2.10 Problems. References. Chapter 3 Turbo Equalization. 3.1 Introduction. 3.2 Context. 3.3 Communication Chain. 3.4 Turbo Decoder: Overview. 3.5 Forward-Backward Algorithm. 3.6 Simplified Algorithm: Interference Canceler. 3.7 Capacity Analysis. 3.8 Blind Turbo Equalization. 3.9 Convergence. 3.10 Multichannel and Multiuser Settings. 3.11 Concluding Remarks. 3.12 Problems. References. Chapter 4 Subspace Tracking for Signal Processing. 4.1 Introduction. 4.2 Linear Algebra Review. 4.3 Observation Model and Problem Statement. 4.4 Preliminary Example: Oja's Neuron. 4.5 Subspace Tracking. 4.6 Eigenvectors Tracking. 4.7 Convergence and Performance Analysis Issues. 4.8 Illustrative Examples. 4.9 Concluding Remarks. 4.10 Problems. References. Chapter 5 Particle Filtering. 5.1 Introduction. 5.2 Motivation for Use of Particle Filtering. 5.3 The Basic Idea. 5.4 The Choice of Proposal Distribution and Resampling. 5.5 Some Particle Filtering Methods. 5.6 Handling Constant Parameters. 5.7 Rao-Blackwellization. 5.8 Prediction. 5.9 Smoothing. 5.10 Convergence Issues. 5.11 Computational Issues and Hardware Implementation. 5.12 Acknowledgments. 5.13 Exercises. References. Chapter 6 Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems. 6.1 Introduction. 6.2 Back-Propagation and Support Vector Machine-Learning Algorithms: Review. 6.3 Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation. 6.4 The Extended Kalman Filter. 6.5 Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms. 6.6 Concluding Remarks. 6.7 Problems. References. Chapter 7 Bandwidth Extension of Telephony Speech. 7.1 Introduction. 7.2 Organization of the Chapter. 7.3 Nonmodel-Based Algorithms for Bandwidth Extension. 7.4 Basics. 7.5 Model-Based Algorithms for Bandwidth Extension. 7.6 Evaluation of Bandwidth Extension Algorithms. 7.7 Conclusion. 7.8 Problems. References. Index.