LESSONS IN ESTIMATION THEORY FOR SIGNAL PROCESSING

09-11-2014, 21:05
Aukcja w czasie sprawdzania była zakończona.

Cena kup teraz: 150 zł

Użytkownik bazar-wiedzy

numer aukcji: 4688717879

Miejscowość Warszawa

Koniec: 09-11-2014, 20:00

Dodatkowe informacje:
Stan: Nowy
Okładka: miękka
Rok wydania (xxxx): 1995
Kondycja: bez śladów używania

Polecamy książki dla fachowców w najtańszych cenach na Allegro

Pamiętaj zawsze możesz do nas zadzwonić
533 [zasłonięte] 038
/ w dni powszednie od 10 do 17 / lub napisać
[zasłonięte]@ksiegarnia-fachowa.pl

LESSONS IN ESTIMATION THEORY FOR SIGNAL PROCESSING, COMMUNICATIONS, AND CONTROL

JERRY M. MENDEL

PRENTICE HALL, 592 STRONY,2ND ED.

An introduction to the field of estimation theory. It is in a lesson format and so can be used for self-study or in a one semester course. The computation that is essential in order to use the estimation algorithms, is associated with MATLAB and its associated tool boxes. -

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.

I