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Digital Spectral Analysis : Parametric, Non-parametric and Advanced Methods.
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 under...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Wiley,
2013.
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| Colección: | ISTE.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright Page; Table of Contents; Preface; PART 1. TOOLS AND SPECTRAL ANALYSIS; Chapter 1. Fundamentals; 1.1. Classes of signal; 1.1.1. Deterministic signals; 1.1.2. Random signals; 1.2. Representations of signals; 1.2.1. Representations of deterministic signals; 1.2.2. Representations of random signals; 1.3. Spectral analysis: position of the problem; 1.4. Bibliography; Chapter 2. Digital Signal Processing; 2.1. Introduction; 2.2. Transform properties; 2.2.1. Some useful functions and series; 2.2.2. Fourier transform; 2.2.3. Fundamental properties; 2.2.4. Convolution sum.
- 2.2.5. Energy conservation (Parseval's theorem)2.2.6. Other properties; 2.2.7. Examples; 2.2.8. Sampling; 2.2.9. Practical calculation, FFT; 2.3. Windows; 2.4. Examples of application; 2.4.1. LTI systems identification; 2.4.2. Monitoring spectral lines; 2.4.3. Spectral analysis of the coefficient of tide fluctuation; 2.5. Bibliography; Chapter 3. Introduction to Estimation Theory with Application in Spectral Analysis; 3.1. Introduction; 3.1.1. Problem statement; 3.1.2. Cramér-Rao lower bound; 3.1.3. Sequence of estimators; 3.1.4. Maximum likelihood estimation; 3.2. Covariance-based estimation.
- 3.2.1. Estimation of the autocorrelation functions of time series3.2.2. Analysis of estimators based on ĉxx(m); 3.2.3. The case of multi-dimensional observations; 3.3. Performance assessment of some spectral estimators; 3.3.1. Periodogram analysis; 3.3.2. Estimation of AR model parameters; 3.3.3. Estimation of a noisy cisoid by MUSIC; 3.3.4. Conclusion; 3.4. Bibliography; Chapter 4. Time-Series Models; 4.1. Introduction; 4.2. Linear models; 4.2.1. Stationary linear models; 4.2.2. Properties; 4.2.3. Non-stationary linear models; 4.3. Exponential models; 4.3.1. Deterministic model.
- 4.3.2. Noisy deterministic model4.3.3. Models of random stationary signals; 4.4. Nonlinear models; 4.5. Bibliography; PART 2. NON-PARAMETRIC METHODS; Chapter 5. Non-Parametric Methods; 5.1. Introduction; 5.2. Estimation of the power spectral density; 5.2.1. Filter bank method; 5.2.2. Periodogram method; 5.2.3. Periodogram variants; 5.3. Generalization to higher-order spectra; 5.4. Bibliography; PART 3. PARAMETRIC METHODS; Chapter 6. Spectral Analysis by Parametric Modeling; 6.1. Which kind of parametric models?; 6.2. AR modeling; 6.2.1. AR modeling as a spectral estimator.
- 6.2.2. Estimation of AR parameters6.3. ARMA modeling; 6.3.1. ARMA modeling as a spectral estimator; 6.3.2. Estimation of ARMA parameters; 6.4. Prony modeling; 6.4.1. Prony model as a spectral estimator; 6.4.2. Estimation of Prony parameters; 6.5. Order selection criteria; 6.6. Examples of spectral analysis using parametric modeling; 6.7. Bibliography; Chapter 7. Minimum Variance; 7.1. Principle of the MV method; 7.2. Properties of the MV estimator; 7.2.1. Expressions of the MV filter; 7.2.2. Probability density of the MV estimator; 7.2.3. Frequency resolution of the MV estimator.