Digital Spectral Analysis Parametric Non Parametric and Advanced Methods 1st Edition by Francis Castanié ISBN 9781848212770 1848212771

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Product details: 

ISBN 10: 1848212771

ISBN 13:  9781848212770

Author: Francis Castanié 

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.

Table of contents: 

PART 1: TOOLS AND SPECTRAL ANALYSIS

Chapter 1. Fundamentals

• Author: Francis CASTANIÉ
  1.1. Classes of signals
  1.2. Representations of signals
  1.3. Spectral analysis: position of the problem
  1.4. Bibliography

Chapter 2. Digital Signal Processing

• Author: Éric LE CARPENTIER
  2.1. Introduction
  2.2. Transform properties
  2.3. Windows
  2.4. Examples of application
  2.5. Bibliography

Chapter 3. Introduction to Estimation Theory with Application in Spectral Analysis

• Authors: Olivier BESSON and André FERRARI
  3.1. Introduction
  3.2. Covariance-based estimation
  3.3. Performance assessment of some spectral estimators
  3.4. Bibliography

Chapter 4. Time-Series Models

• Author: Francis CASTANIÉ
  4.1. Introduction
  4.2. Linear models
  4.3. Exponential models
  4.4. Nonlinear models
  4.5. Bibliography

PART 2: NON-PARAMETRIC METHODS

Chapter 5. Non-Parametric Methods

• Author: Éric LE CARPENTIER
  5.1. Introduction
  5.2. Estimation of the power spectral density
  5.3. Generalization to higher-order spectra
  5.4. Bibliography

PART 3: PARAMETRIC METHODS

Chapter 6. Spectral Analysis by Parametric Modeling

• Authors: Corinne MAILHES and Francis CASTANIÉ
  6.1. Which kind of parametric models?
  6.2. AR modeling
  6.3. ARMA modeling
  6.4. Prony modeling
  6.5. Order selection criteria
  6.6. Examples of spectral analysis using parametric modeling
  6.7. Bibliography

Chapter 7. Minimum Variance

• Author: Nadine MARTIN
  7.1. Principle of the MV method
  7.2. Properties of the MV estimator
  7.3. Link with the Fourier estimators
  7.4. Link with a maximum likelihood estimator
  7.5. Lagunas methods: normalized MV and generalized MV
  7.6. A new estimator: the CAPNORM estimator
  7.7. Bibliography

Chapter 8. Subspace-Based Estimators and Application to Partially Known Signal Subspaces

• Authors: Sylvie MARCOS and Rémy BOYER
  8.1. Model, concept of subspace, definition of high resolution
  8.2. MUSIC
  8.3. Determination criteria of the number of complex sine waves
  8.4. The MinNorm method
  8.5. “Linear” subspace methods
  8.6. The ESPRIT method
  8.7. Illustration of the subspace-based methods performance
  8.8. Adaptive research of subspaces
  8.9. Integrating a priori known frequencies into the MUSIC criterion
  8.10. Bibliography

---

PART 4: ADVANCED CONCEPTS

Chapter 9. Multidimensional Harmonic Retrieval: Exact, Asymptotic, and Modified Cramér-Rao Bounds

• Author: Rémy BOYER
  9.1. Introduction
  9.2. CanDecomp/Parafac decomposition of the multidimensional harmonic model
  9.3. CRB for the multidimensional harmonic model
  9.4. Modified CRB for the multidimensional harmonic model
  9.5. Conclusion
  9.6. Appendices
  9.7. Bibliography

Chapter 10. Introduction to Spectral Analysis of Non-Stationary Random Signals

• Authors: Corinne MAILHES and Francis CASTANIÉ
  10.1. Evolutive spectra
  10.2. Non-parametric spectral estimation
  10.3. Parametric spectral estimation
  10.4. Bibliography

Chapter 11. Spectral Analysis of Non-uniformly Sampled Signals

• Authors: Arnaud RIVOIRA and Gilles FLEURY
  11.1. Applicative context
  11.2. Theoretical framework
  11.3. Generation of a randomly sampled stochastic process
  11.4. Spectral analysis using undated samples
  11.5. Spectral analysis using dated samples
  11.6. Perspectives
  11.7. Bibliography

Chapter 12. Space–Time Adaptive Processing

• Authors: Laurent SAVY and François LE CHEVALIER
  12.1. STAP, spectral analysis, and radar signal processing
  12.2. Space–time processing as a spectral estimation problem
  12.3. STAP architectures
  12.4. Relative advantages of pre-Doppler and post-Doppler STAP
  12.5. Conclusion
  12.6. Bibliography
  12.7. Glossary

Chapter 13. Particle Filtering and Tracking of Varying Sinusoids

• Author: David BONACCI
  13.1. Particle filtering
  13.2. Application to spectral analysis
  13.3. Bibliography

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Tags: Francis Castanié, Digital Spectral, Analysis P, arametric, Parametric and Advanced