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Informationen zum Autor Francis Castanié is an emeritus professor of INPT and Laboratory Director of TeSA in Toulouse, France. Klappentext Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature. The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models. An entire chapter is devoted to the non-parametric methods most widely used in industry. High resolution methods are detailed in a further four chapters: spectral analysis by stationary time series modeling, minimum variance, and subspace-based estimators. Finally, advanced concepts are the core of the last four chapters: spectral analysis of non-stationary random signals, space time adaptive processing: irregularly sampled data processing, particle filtering and tracking of varying sinusoids. Suitable for students, engineers working in industry, and academics at any level, this book provides a rare complete overview of the spectral analysis domain. Zusammenfassung Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature. Inhaltsverzeichnis Preface xiii PART 1. TOOLS AND SPECTRAL ANALYSIS 1 Chapter 1. Fundamentals 3 Francis CASTANIÉ 1.1. Classes of signals 3 1.2. Representations of signals 9 1.3. Spectral analysis: position of the problem 20 1.4. Bibliography 21 Chapter 2. Digital Signal Processing 23 Éric LE CARPENTIER 2.1. Introduction 23 2.2. Transform properties 24 2.3. Windows 49 2.4. Examples of application 57 2.5. Bibliography 64 Chapter 3. Introduction to Estimation Theory with Application in Spectral Analysis 67 Olivier BESSON and André FERRARI 3.1. Introduction 67 3.2. Covariance-based estimation 86 3.3. Performance assessment of some spectral estimators 95 3.4. Bibliography 102 Chapter 4. Time-Series Models 105 Francis CASTANIÉ 4.1. Introduction 105 4.2. Linear models 107 4.3. Exponential models 117 4.4. Nonlinear models 120 4.5. Bibliography 121 PART 2. NON-PARAMETRIC METHODS 123 Chapter 5. Non-Parametric Methods 125 Éric LE CARPENTIER 5.1. Introduction 125 5.2. Estimation of the power spectral density 130 5.3. Generalization to higher-order spectra 141 5.4. Bibliography 142 PART 3. PARAMETRIC METHODS 143 Chapter 6. Spectral Analysis by Parametric Modeling145 Corinne MAILHES and Francis CASTANIÉ 6.1. Which kind of parametric models? 145 6.2. AR modeling 146 6.3. ARMA modeling 154 6.4. Prony modeling 156 6.5. Order selection criteria 158 6.6. Examples of spectral analysis using parametric modeling 162 6.7. Bibliography 166 Chapter 7. Minimum Variance 169 Nadine MARTIN 7.1. Principle of the MV method . . 174 7.2. Properties of the MV estimator 177 7.3. Link with the Fourier estimators 188 7.4. Link with a maximum likelihood estimator 190 7.5. Lagunas methods: normalized MV and generalized MV 192 7.6. A new estimator: the CAPNORM estimator 200 7.7. Bibliography 204 Chapter 8. Subspace-Based Estimators and Application to Partially Known Signal Subspaces 207 Sylvie MARCOS and Rémy BOYER 8....
