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Full Description
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.
Contents
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.
