Modern information systems must handle huge amounts of data having varied natural or technological origins. Automated processing of these increasing loads of signals requires training specialists capable of formalising the problems encountered. This book aims at supplying a formalised, concise presentation of the basis of statistical signal processing. Similar interest is directed to aspects related to signal modelling and to signal estimation. So, in order to supply the reader with the desirable theoretical bases and to allow him to make progress in the discipline, most of the results presented here are carefully justified. First, the representation of random signals in the Fourier domain and their filtering are considered. These tools enable linear prediction theory and related classical filtering techniques to be addressed in a simple way. Then the spectrum identification problem is presented as a first step toward spectrum estimation, which is studied in the non-parametric and in the parametric frameworks. The last chapters introduce synthetically further advanced techniques that will enable the reader to solve signal processing problems of a general nature. Rather than supplying an exhaustive description of existing techniques, this book is designed for students, scientists and research engineers interested in statistical signal processing who need to acquire the necessary bases to address the specific problems that they may be faced with, as well as the corresponding literature. The CD-ROM contains MATLABÊ<ha programs in HTML format and is intended to provide simulation examples (program listings + simulation results) In addition, it also presents some basics of probability.

Contents

Introduction.- Random Processes.- Power Spectrum.- Spectral Representation.- Filtering.- Important Particular Processes.- Non-linear Transforms of Processes.- Linear Prediction.- Particular Filtering Techniques.- Rational Spectral Densities.- Spectral Identification.- Non-parametric Spectral Estimation.- Parametric Spectral Estimation.- Higher-order Statistics.- Bayesian and MCMC Methods.- Adaptive Estimation.- Appendices A-Z.