Über die Autorin bzw. den Autor

SIMON HAYKIN, PhD, is University Professor and Director of the Adaptive Systems Laboratory at McMaster University.

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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

Aus dem Klappentext

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

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Unsupervised Adaptive Filtering, Volume 2, Blind Deconvolution

John Wiley & Sons

Contents

Contributors................................................................................................................................................................................................................................................viiPreface.....................................................................................................................................................................................................................................................xi1 Introduction Simon Haykin...............................................................................................................................................................................................................................11.1 Why Adaptive Filtering?.................................................................................................................................................................................................................................11.2 Supervised and Unsupervised Forms of Adaptive Filtering.................................................................................................................................................................................................21.3 Two Important Unsupervised Signal-Processing Tasks......................................................................................................................................................................................................31.4 Three Fundamental Approaches to Unsupervised Adaptive Filtering.........................................................................................................................................................................................61.5 Organization of Volume II...............................................................................................................................................................................................................................10References..................................................................................................................................................................................................................................................112 The Core of FSE-CMA Behavior Theory 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.....................132.1 Introduction............................................................................................................................................................................................................................................142.2 MMSE Equalization and LMS...............................................................................................................................................................................................................................222.3 The CM Criterion and CMA................................................................................................................................................................................................................................412.4 CMA-Adapted-Equalizer Design Issues with Illustrative Examples..........................................................................................................................................................................................752.5 Case Studies............................................................................................................................................................................................................................................892.6 Conclusions.............................................................................................................................................................................................................................................106References..................................................................................................................................................................................................................................................1083 Relationships between Blind Deconvolution and Blind Source Separation Scott C. Douglas and Simon Haykin.................................................................................................................................................1133.1 Introduction............................................................................................................................................................................................................................................1133.2 Problem Descriptions....................................................................................................................................................................................................................................1173.3 Algorithmic Relationships...............................................................................................................................................................................................................................1223.4 Structural Relationships................................................................................................................................................................................................................................1293.5 Extensions..............................................................................................................................................................................................................................................1403.6 Conclusions.............................................................................................................................................................................................................................................142References..................................................................................................................................................................................................................................................1424 Blind Separation of Independent Sources Based on Multiuser Kurtosis Optimization Criteria Constantinos B. Papadias......................................................................................................................................1474.1 Introduction............................................................................................................................................................................................................................................1484.2 Problem Formulation and Assumptions.....................................................................................................................................................................................................................1504.3 Review: The Single-User Equalization Problem............................................................................................................................................................................................................1544.4 Necessary and Su1/2cient Conditions for...