Modern information systems must handle huge amounts of data having
varied natural or technological origins. Automated processing of these
increasing signal loads requires the training of specialists capable of
formalising the problems encountered. This book supplies a formalised,
concise presentation of the basis of statistical signal processing.
Equal emphasis is placed on approaches related to signal modelling and
to signal estimation. In order to supply the reader with the desirable
theoretical fundamentals and to allow him to make progress in the
discipline, the results presented here are carefully justified. 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. The spectrum identification problem is presented as a first step
toward spectrum estimation, which is studied in non-parametric and
parametric frameworks. The later 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 and who need to acquire the necessary grounding to
address the specific problems with which they may be faced. It also
supplies a well-organized introduction to the literature.