Ebook: Statistical Signal Processing: Modelling and Estimation

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.

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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.

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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.
Content:
Front Matter....Pages i-xx
Introduction....Pages 1-8
Random Processes....Pages 9-21
Power Spectrum of WSS Processes....Pages 23-29
Spectral Representation of WSS Processes....Pages 31-39
Filtering of WSS Processes....Pages 41-49
Important Particular Processes....Pages 51-68
Non-linear Transforms of Processes....Pages 69-78
Linear Prediction of WSS Processes....Pages 79-94
Particular Filtering Techniques....Pages 95-110
Rational Spectral Densities....Pages 111-117
Spectral Identification of WSS Processes....Pages 119-138
Non-parametric Spectral Estimation....Pages 139-158
Parametric Spectral Estimation....Pages 159-184
Higher Order Statistics....Pages 185-201
Bayesian Methods and Simulation Techniques....Pages 203-230
Adaptive Estimation....Pages 231-248
Back Matter....Pages 249-331

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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.
Content:
Front Matter....Pages i-xx
Introduction....Pages 1-8
Random Processes....Pages 9-21
Power Spectrum of WSS Processes....Pages 23-29
Spectral Representation of WSS Processes....Pages 31-39
Filtering of WSS Processes....Pages 41-49
Important Particular Processes....Pages 51-68
Non-linear Transforms of Processes....Pages 69-78
Linear Prediction of WSS Processes....Pages 79-94
Particular Filtering Techniques....Pages 95-110
Rational Spectral Densities....Pages 111-117
Spectral Identification of WSS Processes....Pages 119-138
Non-parametric Spectral Estimation....Pages 139-158
Parametric Spectral Estimation....Pages 159-184
Higher Order Statistics....Pages 185-201
Bayesian Methods and Simulation Techniques....Pages 203-230
Adaptive Estimation....Pages 231-248
Back Matter....Pages 249-331
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