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In the framework of Digital Signal Processing, the Parametric Modeling of Signals offers as a natural byproduct a class of Spectral tools, namely the so-called Rational Spectra. This class of Spectra (and their associated Estimators) applies to the case of Stationary as well as Non-Stationary Signals and Random Processes. This important text brings together the significant advances that have been achieved over the last ten years from the confrontation of the theory and its variety of applications.
Sommario
1. Principle Reminders:1.1 Classical (2nd order) Spectral Analysis by Stationary Time Series Linear Modeling
Parameter Estimation Principles in the Stationary framework:
-Least Mean Square
- Maximum Likelihood
-Minimum Variance,
-Subspace Based Methods
-Rational Approximants
1.2 Higher Order Spectra Extensions
For the minority of methods for which the extensions presently exist, they will be described.
1.3 Non Stationary Random Processes Extensions:
- Various Evolutive Spectra Definitions
-Parametric Models for Non-Stationary Signals
-Parameter Estimation in the Non-Stationary framework
2 Recent Advances of the methods
3 Selected Applications Examples
3.1 In Telecommunications
3.2 In Navigation and Positioning
3.3 In Aeronautics Vibrating Structure monitoring
3.4 In Biomedical Signal Processing
3.5 In Radar for Automotive Applications
Riassunto
In the framework of Digital Signal Processing, the Parametric Modeling of Signals offers as a natural byproduct a class of Spectral tools, namely the so-called Rational Spectra. This class of Spectra (and their associated Estimators) applies to the case of Stationary as well as Non-Stationary Signals and Random Processes.
