| Description |
1 online resource (401 pages) |
| Series |
ISTE |
|
ISTE
|
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
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 a |
| Notes |
7.3. Link with the Fourier estimators |
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Print version record |
| Subject |
Spectral theory (Mathematics)
|
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Signal processing -- Digital techniques -- Mathematics.
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Spectrum analysis.
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Signal processing -- Digital techniques -- Mathematics
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Spectral theory (Mathematics)
|
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Spectrum analysis
|
| Form |
Electronic book
|
| ISBN |
9781118601839 |
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1118601831 |
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