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

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