Cargando…
Spectral Analysis : Parametric and Non-parametric Digital Methods.
This book deals with these parametric methods, first discussing those based on time series models, Capon's method and its variants, and then estimators based on the notions of sub-spaces. However, the book also deals with the traditional "analog" methods, now called non-parametric met...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Wiley,
2010.
|
| Colección: | ISTE.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Spectral Analysis; Table of Contents; Preface; Specific Notations; PART I. Tools and Spectral Analysis; Chapter 1. Fundamentals; 1.1. Classes of signals; 1.1.1. Deterministic signals; 1.1.2. Random signals; 1.2. Representations of signals; 1.2.1. Representations of deterministic signals; 1.2.1.1. Complete representations; 1.2.1.2. Partial representations; 1.2.2. Representations of random signals; 1.2.2.1. General approach; 1.2.2.2. 2nd order representations; 1.2.2.3. Higher order representations; 1.3. Spectral analysis: position of the problem; 1.4. Bibliography.
- Chapter 2. Digital Signal Processing2.1. 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. Estimation in Spectral Analysis.
- 3.1. Introduction to estimation3.1.1. Formalization of the problem; 3.1.2. Cramér-Rao bounds; 3.1.3. Sequence of estimators; 3.1.4. Maximum likelihood estimation; 3.2. Estimation of 1st and 2nd order moments; 3.3. Periodogram analysis; 3.4. Analysis of estimators based on cxx (m); 3.4.1. Estimation of parameters of an AR model; 3.4.2. Estimation of a noisy cisoid by MUSIC; 3.5. Conclusion; 3.6. Bibliography; Chapter 4. Time-Series Models; 4.1. Introduction; 4.2. Linear models; 4.2.1. Stationary linear models; 4.2.2. Properties; 4.2.2.1. Stationarity; 4.2.2.2. Moments and spectra.
- 4.2.2.3. Relation with Wold's decomposition4.2.3. Non-stationary linear models; 4.3. Exponential models; 4.3.1. Deterministic model; 4.3.2. Noisy deterministic model; 4.3.3. Models of random stationary signals; 4.4. Non-linear models; 4.5. Bibliography; PART II. 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 III. Parametric Methods.
- Chapter 6. Spectral Analysis by Stationary Time Series Modeling6.1. Parametric models; 6.2. Estimation of model parameters; 6.2.1. Estimation of AR parameters; 6.2.2. Estimation of ARMA parameters; 6.2.3. Estimation of Prony parameters; 6.2.4. Order selection criteria; 6.3. Properties of spectral estimators produced; 6.4. 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.