Angielski
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: 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 nonlinear signal processing algorithms described in the book to an application of interest is available on the accompanying FTP site.
