Nonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are n--Gaussian, rather than Gaussian, processes. Notably, by concentrating on just two n--Gaussian models, a large set of tools is developed that encompass a large portion of the nlinear signal processing tools proposed in the literature over the past several decades. Key features include: aeo Numerous problems at the end of each chapter to aid development and understanding aeo 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 aeo A set of 60+ MATLAB software m--files allowing the reader to quickly design and apply any of the nlinear signal processing algorithms described in the book to an application of interest is available on the accompanying FTP site.
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.