Statistical Signal Processing
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.
Formal mathematical treatment of an advanced area of signal processing including many specially-written end-of-chapter excercisesTeaches a wide variety of techniques necessary for modern applications of signal processing and demonstrates how the techniques can be applied to a wide variety of situations by the student himself without recourse to an exhaustive list of when to use which techniqueThe only book on this subject at this level for students specialising in this area