Modeling, Estimation and Optimal Filtration in Signal Processing
2010-ж. янв. · ISTE 25-китеп · John Wiley & Sons саткан
Электрондук китеп
400
Барактар
Учкай маалымат
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 their variants.
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 their variants.
Автор жөнүндө
Mohamed Najim is Professor in signal processing at the ENSEIRB and Université Bordeaux I (France), where he leads the Signal and Image Processing group.
An IEEE Fellow, he has worked in adaptive control and in the field of 1D and n-D identification in signal and image processing.
An IEEE Fellow, he has worked in adaptive control and in the field of 1D and n-D identification in signal and image processing.
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