Nonlinear Signal Processing: A Statistical Approach

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9780471676249: Nonlinear Signal Processing: A Statistical Approach

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.

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From the Back Cover:

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.

About the Author:

GONZALO R. ARCE received a PhD degree in electrical engineering from Purdue University in 1982. Since 1982, he has been with the faculty of the Department of Electrical and Computer Engineering at the University of Delaware where he is currently Charles Black Evans Distinguished Professor and Chairman. He has held visiting professor appointments at the Unisys Corporate Research Center and at the International Center for Signal and Image Processing, Tampere University of Technology, in Tampere, Finland. He holds seven U.S. patents, and his research has been funded by DoD, NSF, and numerous industrial organizations. He is an IEEE Fellow for his contributions to the theory and applications of nonlinear signal processing.

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