Del 24 i serien Unsupervised Adaptive Filtering

Unsupervised Adaptive Filtering, Blind Deconvolution

Inbunden, Engelska, 2000

1 989 kr

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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

Produktinformation

Utgivningsdatum2000-04-22
Mått161 x 242 x 19 mm
Vikt465 g
FormatInbunden
SpråkEngelska
SerieUnsupervised Adaptive Filtering
Antal sidor200
FörlagJohn Wiley & Sons Inc
ISBN9780471379416

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Elektronik och kommunikationer inom Naturvetenskap och teknik

Contributors viiPreface xi1 Introduction 1Simon Haykin1.1 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 13C. 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. Treichler2.1 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 113Scott C. Douglas and Simon Haykin3.1 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 147Constantinos B. Papadias4.1 Introduction 1484.2 Problem Formulation and Assumptions 1504.3 Review: The Single-User Equalization Problem 1544.4 Necessary and Su½cient Conditions for BSS 1604.5 Unconstrained Criteria: The MU-CM Approach 1624.6 Constrained Criteria: The MUK Approach 1654.7 Numerical Examples 1714.8 Conclusions 175References 176Index 181