Unsupervised Adaptive Filtering, Blind Deconvolution

Contributors vii

Preface xi

1 Introduction 1
Simon Haykin

1.1 Why Adaptive Filtering?  1

1.2 Supervised and Unsupervised Forms of Adaptive Filtering 2

1.3 Two Important Unsupervised Signal-Processing Tasks 3

1.4 Three Fundamental Approaches to Unsupervised Adaptive Filtering 6

1.5 Organization of Volume II 10

References 11

2 The Core of FSE-CMA Behavior Theory 13
C. R. Johnson, Jr., P. Schniter, I. Fijalkow, L. Tong, J. D. Behm, M. G. Larimore, D. R. Brown, R. A. Casas, T. J. Endres, S. Lambotharan, A. Touzni, H. H. Zeng, M. Green, and J. R. Treichler

2.1 Introduction 14

2.2 MMSE Equalization and LMS 22

2.3 The CM Criterion and CMA 41

2.4 CMA-Adapted-Equalizer Design Issues with Illustrative Examples 75

2.5 Case Studies 89

2.6 Conclusions 106

References 108

3 Relationships between Blind Deconvolution and Blind Source Separation 113
Scott C. Douglas and Simon Haykin

3.1 Introduction 113

3.2 Problem Descriptions 117

3.3 Algorithmic Relationships 122

3.4 Structural Relationships 129

3.5 Extensions 140

3.6 Conclusions 142

References 142

4 Blind Separation of Independent Sources Based on Multiuser Kurtosis Optimization Criteria 147
Constantinos B. Papadias

4.1 Introduction 148

4.2 Problem Formulation and Assumptions 150

4.3 Review: The Single-User Equalization Problem 154

4.4 Necessary and Su½cient Conditions for BSS 160

4.5 Unconstrained Criteria: The MU-CM Approach 162

4.6 Constrained Criteria: The MUK Approach 165

4.7 Numerical Examples 171

4.8 Conclusions 175

References 176

Index 181