Nonlinear Filtering

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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. Inhaltsverzeichnis 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