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Modeling, estimation and optimal filtering in signal processing /
The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing.Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In a...
| Cote: | Libro Electrónico |
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| Auteur principal: | |
| Format: | Électronique eBook |
| Langue: | Inglés Francés |
| Publié: |
London ; ISTE ; Hoboken, NJ :
J. Wiley & Sons,
2008.
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| Collection: | Digital signal and image processing series.
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| Sujets: | |
| Accès en ligne: | Texto completo (Requiere registro previo con correo institucional) |
Table des matières:
- Parametric models
- Least squares estimation of parameters of linear models
- Matched and Wiener filters
- Adaptive filtering
- Kalman filtering
- Application of the Kalman filter to signal enhancement
- Estimation using the instrumental variable technique
- H [infinity symbol] estimation : an alternative to Kalman filtering?
- Introduction to particle filtering
- Karhunen Loeve transform
- Subspace decomposition for spectral analysis
- Subspace decomposition applied to speech enhancement
- From AR parameters to line spectrum pair
- Influence of an additive white noise on the estimation of AR parameters
- The Schur-Cohn algorithm
- The gradient method
- An alternative way of understanding Kalman filtering
- Calculation of the Kalman gain using the Mehra approach
- Calculation of the Kalman gain (the Carew and Belanger method)
- The unscented Kalman filter (UKF).