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...

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Bibliographic Details
Main Author: Najim, Mohamed
Format: eBook
Language:English
Published: London J. Wiley & Sons 2008
Series:Digital signal and image processing series
Subjects:
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
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130 0 |a Modelisation, estimation et filtrage optimal en traitement du signal 
245 0 0 |a Modeling, estimation and optimal filtering in signal processing  |c Mohamed Najim 
260 |a London  |b J. Wiley & Sons  |c 2008 
300 |a xv, 392 pages  |b illustrations 
505 0 |a Includes bibliographical references and index 
505 0 |a 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) 
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653 |a Filtres numériques 
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520 |a 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 addition, sinusoidal models are addressed.Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented.Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and the