Description
Book SynopsisA 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.
Table of ContentsContributors 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
