Modeling, Estimation and Optimal Filtering in Signal Processing
Publication Date: April 2008 Hardback 416 pp.
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.
It begins by introducing discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations, before addressing sinusoidal models. Estimation approaches based on least squares methods and instrumental variable techniques are then presented. Finally, the book deals with optimal filters, such as Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and their variants.
1. Parametric Models.
2. Least-Squares Estimation of Parameters of Linear Model.
3. Matched and Wiener Filters.
4. Adaptive Filtering.
5. Kalman Filtering.
6. Application of the Kalman Filter to Signal Enhancement.
7. Estimation using the Instrumental Variable Techniques.
8. H Infinity Estimation: An Alternative to Kalman Filtering?
9. Introduction to Particle Filtering.
About the Authors
Mohamed Najim is Professor in Signal Processing at the ENSEIRB and University of Bordeaux I, France, where he leads the Signal and Image Processing group.
An IEEE Fellow since 1989, he has worked in various fields including microwaves, modeling and identification, adaptive filtering (including H infinity), adaptive control and in the field of 1D and n-D identification in signal and image processing.
He has published several books, more than 220 technical papers and has taught courses in digital signal processing for more than 30 years.