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.

- Cover Page
- Title Page
- Copyright
- Table of Contents
- Preface
- Chapter 1: Parametric Models
- Chapter 2: Least Squares Estimation of Parameters of Linear Models
- Chapter 3: Matched and Wiener Filters
- Chapter 4: Adaptive Filtering
- Chapter 5: Kalman Filtering
- Chapter 6: Application of the Kalman Filter to Signal Enhancement
- Chapter 7: Estimation using the Instrumental Variable Technique
- Chapter 8: H∞ Estimation: an Alternative to Kalman Filtering?
- Chapter 9: Introduction to Particle Filtering
- Appendix A: Karhunen Loeve Transform
- Appendix B: Subspace Decomposition for Spectral Analysis
- Appendix C: Subspace Decomposition Applied to Speech Enhancement
- Appendix D: From AR Parameters to Line Spectrum Pair
- Appendix E: Influence of an Additive White Noise on the Estimation of AR Parameters
- Appendix F: The Schur-Cohn Algorithm
- Appendix G: The Gradient Method
- Appendix H: An Alternative Way of Understanding Kalman Filtering
- Appendix I: Calculation of the Kalman Gain using the Mehra Approach
- Appendix J: Calculation of the Kalman Gain (the Carew and Belanger Method)
- Appendix K: The Unscented Kalman Filter (UKF)
- Index