Unsupervised adaptive filters let systems respond and adapt automatically to changing conditions without the need for human supervision. With applications to signal processing, information theory, imaging, and remote sensing, among others, this is an area of intense research. This book describes the current state of the art in the field.

From the Back Cover

A complete, one-stop reference on the state of the act 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. Topics in Volume I include:

Neural and information-theoretic approaches to blind signal separation
Models, concepts, algorithms, and performance of blind source separation
Blind separation of delayed and convolved sources
Blind deconvolution of multipath mixtures
Applications of blind source separation

Volume II: Blind Deconvolution continues coverage with blind channel equalization and its relationship to blind source separation.

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