Adaptive Signal Processing:Next Generation Solutions

The prelims comprise:
Half Title
Wiley Series Page
Title
Copyright
Contents
Preface
Contributors View full abstract»

This chapter contains sections titled:
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 View full abstract»

This chapter contains sections titled:
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 View full abstract»

This chapter contains sections titled:
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 View full abstract»

This chapter contains sections titled:
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 View full abstract»

This chapter contains sections titled:
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 View full abstract»

This chapter contains sections titled:
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 View full abstract»

This chapter contains sections titled:
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 View full abstract»

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