Lessons in Estimation Theory for Signal Processing, Communications, and Control

1. Introduction, Coverage, Philosophy, and Computation. 2. The Linear Model. 3. Least-Squares Estimation: Batch Processing. 4. Least-Squares Estimation: Singular-Value Decomposition. 5. Least-Squares Estimation: Recursive Processing. 6. Small Sample Properties of Estimators. 7. Large Sample Properties of Estimators. 8. Properties of Least-Squares Estimators. 9. Best Linear Unbiased Estimation. 10. Likelihood. 11. Maximum-Likelihood Estimation. 12. Multivariate Gaussian Random Variables. 13. Mean-Squared Estimation of Random Parameters. 14. Maximum A Posteriori Estimation of Random Parameters. 15. Elements of Discrete-Time Gauss-Markov Random Sequences. 16. State Estimation: Prediction. 17. State Estimation: Filtering (The Kalman Filter). 18. State Estimation: Filtering Examples. 19. State Estimation: Steady-State Kalman Filter and Its Relationships to a Digital Wiener Filter. 20. State Estimation: Smoothing. 21. State Estimation: Smoothing (General Results). 22. State Estimation for the Not-So-Basic State-Variable Model. 23. Linearization and Discretization of Nonlinear Systems. 24. Iterated Least Squares and Extended Kalman Filtering. 25. Maximum-Likelihood State and Parameter Estimation. 26. Kalman-Bucy Filtering. A. Sufficient Statistics and Statistical Estimation of Parameters. B. Introduction to Higher-Order Statistics. C. Estimation and Applications of Higher-Order Statistics. D. Introduction to State-Variable Models and Methods. Appendix A: Glossary of Major Results. Appendix B: Estimation of Algorithm M-Files. References. Index.