Nonlinear Filtering Concepts and Engineering Applications

Nonlinear Filtering covers linear and nonlinear filtering in a comprehensive manner, with appropriate theoretic and practical development. Aspects of modeling, estimation, recursive filtering, linear filtering, and nonlinear filtering are presented with appropriate and sufficient mathematics. A modeling-control-system approach is used when applicable, and detailed practical applications are presented to elucidate the analysis and filtering concepts. MATLAB routines are included, and examples from a wide range of engineering applications - including aerospace, automated manufacturing, robotics, and advanced control systems - are referenced throughout the text.

Preface

Acknowledgements

Authors

Introduction

Section I Mathematical Models, Kalman Filtering and H-Infinity Filters

1. Dynamic System Models and Basic Concepts

2. Filtering and Smoothing

3. H∞ Filtering

4. Adaptive Filtering

Section II Factorization and Approximation Filters

5. Factorization Filtering

6. Approximation Filters for Nonlinear Systems

7. Generalized Model Error Estimators for Nonlinear Systems

Section III Nonlinear Filtering, Estimation and Implementation Approaches

8. Nonlinear Estimation and Filtering

9. Nonlinear Filtering Based on Characteristic Functions

10. Implementation Aspects of Nonlinear Filters

11. Nonlinear Parameter Estimation

12. Nonlinear Observers

Section IV Appendixes – Basic Concepts and Supporting Material

Appendix A: System Theoretic Concepts – Controllability, Observability, Identifiability and Estimability

Appendix B: Probability, Stochastic Processes and Stochastic Calculus

Appendix C: Bayesian Filtering

Appendix D: Girsanov Theorem

Appendix E: Concepts from Signal and Stochastic Analyses

Appendix F: Notes on Simulation and Some Algorithms

Appendix G: Additional Examples

Index