Description
Book SynopsisNonlinear 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.
Table of ContentsPreface
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
