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.
ISTE Ltd and John Wiley & Sons Inc
Country of Publication:
06 June 2008
Further / Higher Education
Chapter 1. Introduction to Parametric Models. Chapter 2. Least-Squares Estimation of Linear Model Parameters. Chapter 3. Matched Filters and Wiener Filters. Chapter 4. Adaptive Filters. Chapter 5. Kalman Filters. Chapter 6. Kalman Filtering for Speech Enhancement. Chapter 7. Instrumental Variable Techniques. Chapter 8. H Infinity Techniques: An Alternative to Kalman filters? Chapter 9. Introduction to Particle Filtering. Appendix.
Mohamed Najim is Professor in signal processing at the ENSEIRB and Universite 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.
Reviews for Modeling, Estimation and Optimal Filtration in Signal Processing
This book provides the reader for the first time with a comprehensive collection of the significant results obtained to date in the field of parametric signal modeling and presents a number of new approaches. (Mathematical Reviews, 2010)