Über dieses Produkt

Product Identifiers

PublisherWiley & Sons, Incorporated, John

ISBN-100471676241

ISBN-139780471676249

eBay Product ID (ePID)30777204

Product Key Features

Number of Pages480 Pages

LanguageEnglish

Publication NameNonlinear Signal Processing : a Statistical Approach

SubjectWaves & Wave Mechanics, Signals & Signal Processing, Probability & Statistics / General

Publication Year2004

TypeTextbook

Subject AreaMathematics, Technology & Engineering, Science

AuthorGonzalo R. Arce

FormatHardcover

Dimensions

Item Height1.1 in

Item Weight29.2 Oz

Item Length9.5 in

Item Width6.3 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2004-042240

Dewey Edition22

Reviews"This comprehensive book...will be a good reference for both the trained statisticians and engineers." ( Technometrics , February 2006), "This book will be a valuable textbook for students and researchers interested in learning nonlinear signal processing techniques designed to be robust against heavy-tailed error distributions." ( Journal of the American Statistician , December 2008) "This comprehensive book...will be a good reference for both the trained statisticians and engineers." ( Technometrics , February 2006)

Number of Volumes1 vol.

IllustratedYes

Dewey Decimal621.382/2

SynopsisNonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nonlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are non-Gaussian, rather than Gaussian, processes., A Unified Treatment of Non-Gaussian Processes and Nonlinear Signal Processing Nonlinear signal processing methods are finding numerous applications in such fields as imaging, teletraffic, communications, hydrology, geology, and economics fields where nonlinear systems and non-Gaussian processes emerge. Within a broad class of nonlinear signal processing methods, this book provides a unified treatment of optimal and adaptive signal processing tools that mirror those of Wiener and Widrow, extensively presented in the linear filter theory literature. The methods detailed in this book can thus be tailored to effectively exploit non-Gaussian signal statistics in a system or its inherent nonlinearities to overcome many of the limitations of the traditional practices used in signal processing. Chapters include: A review of non-Gaussian models, with an emphasis on the class of generalized Gaussian distributions and the class of stable distributions The basic principles of order statistics Maximum likelihood and robust estimation principles Signal processing tools based on weighted medians and stack filters Filters based on linear combinations of order statistics and various generalizations Signal processing methods tailored for signals described by stable distributions Numerous problems, examples, and case studies enable rapid mastery of the topics discussed, and over 60 MATLAB m-files allow the reader to quickly design and apply the algorithms to any application., Nonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nonlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are non-Gaussian, rather than Gaussian, processes. Notably, by concentrating on just two non-Gaussian models, a large set of tools is developed that encompass a large portion of the nonlinear signal processing tools proposed in the literature over the past several decades. Key features include: * Numerous problems at the end of each chapter to aid development and understanding * Examples and case studies provided throughout the book in a wide range of applications bring the text to life and place the theory into context * A set of 60+ MATLAB software m-files allowing the reader to quickly design and apply any of the nonlinear signal processing algorithms described in the book to an application of interest is available on the accompanying FTP site.

LC Classification NumberTK5102.9.A77 2004