Advances in State and Parameter Estimation Theory and Practice

1. Introduction 2. Least Squares and Maximum-Likelihood Methods 3. Kalman Filtering Methods 4. Filtering-cum-Data Fusion with State Delay and Missing Measurements 5. Gaussian-Sum Extended Kalman Filter with Lyapunov Stability Analysis 6. Gaussian-sum Information Filter with Lyapunov Stability Analysis 7. Image Centroid Tracking with Square-Root Kalman Filters 8. Image Centroid Tracking with Fuzzy-Logic-Augmented Filters 9. Image Centroid Tracking-cum-Fusion using New Factorization Filtering Algorithms 10. H-Infinity Fuzzification Filtering and Target Tracking (TT) 11. H-Infinity-Based Observer 12. Deterministic Nonlinear Estimators-Observers and Stability Results 13. Hybrid Global H-Infinity Filter 14. Neural Networks for Parameter Estimation with Lyapunov Stability 15. Interactive Multiple Modelling for Target Tracking with New Algorithms 16. Machine Learning for Estimation Appendix A Appendix B Appendix C