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Lessons in Estimation Theory for Signal Processing, Communications, and Control, 2/E
Jerry M. MendelUniversity of Southern California, Los Angeles

ISBN-10: 0131209817
ISBN-13:  9780131209817

Publisher:  Prentice Hall
Copyright:  1995
Format:  Paper; 592 pp
Published:  03/14/1995
Status: Available on Demand   What's this?



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.

  • 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.
  • begins each lesson with a summary/objectives and includes lesson multiple-choice summary questions for review.
  • summarizes important results in theorems and corollaries.
  • includes problems for all lessons, including computational problems that can only be carried out using a computer.
  • links computations into MATLAB…<194> 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.



 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.

Estimation theory is a product of need and technology. As a result, it is an integral part of many branches of science and engineering. To help readers differentiate among the rich collection of estimation methods and algorithms, this book describes in detail many of the important estimation methods and shows how they are interrelated. Written as a collection of lessons, this book introduces readers o the general field of estimation theory and includes abundant supplementary material.

Solutions Manual Lessons in Estimation Theory for Signal Processing, Communications, and Control, 2/E
Mendel
©1995 | Prentice Hall | Paper | Available on Demand   What's this?
ISBN-10: 0137147058 | ISBN-13: 9780137147052


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