Overview

This book covers key topics in parameter estimation and state estimation, with supplemental lessons on sufficient statistics and statistical estimation of parameters, higher-order statistics and a review of state variable models. It also links computations into MATLAB and its associated toolboxes. A small number of important estimation M-files, which do not presently appear in any MathWork's toolbox, are included in an appendix.

Full Product Details

Author:   Jerry Mendel
Publisher:   Pearson Education (US)
Imprint:   Prentice Hall
Edition:   2nd edition
Dimensions:   Width: 24.00cm , Height: 3.00cm , Length: 18.00cm
Weight:   0.900kg
ISBN:  

9780131209817

ISBN 10:   0131209817
Pages:   592
Publication Date:   01 July 1994
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   In Print  
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of Contents

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.

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