Unsupervised Adaptive Filtering, Blind Deconvolution

A complete, one-stop reference on the state of the art of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields. This book brings together cutting-edge information previously available only in disparate papers and articles, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind channel equalization algorithms. Divided into two volumes for ease of presentation, this important work shows how these algorithms, although developed independently, are closely related foundations of unsupervised adaptive filtering. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications in diverse fields. More than 100 illustrations as well as case studies, appendices, and references further enhance this excellent resource. Following coverage begun in Volume I: Blind Source Separation, this volume discusses: * The core of FSE-CMA behavior theory * Relationships between blind deconvolution and blind source separation * Blind separation of independent sources based on multiuser kurtosis optimization criteria

Table of Contents:
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