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

Book Description

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.

Table of Contents

  1. Title Page
  2. Copyright Page
  3. Contents
  4. Preface
  5. Lesson 1 Introduction, Coverage, Philosophy, and Computation
  6. Lesson 2 The Linear Model
  7. Lesson 3 Least-squares Estimation: Batch Processing
  8. Lesson 4 Least-squares Estimation: Singular-value Decomposition
  9. Lesson 5 Least-squares Estimation: Recursive Processing
  10. Lesson 6 Small-sample Properties of Estimators
  11. Lesson 7 Large-sample Properties of Estimators
  12. Lesson 8 Properties of Least-squares Estimators
  13. Lesson 9 Best Linear Unbiased Estimation
  14. Lesson 10 Likelihood
  15. Lesson 11 Maximum-likelihood Estimation
  16. Lesson 12 Multivariate Gaussian Random Variables
  17. Lesson 13 Mean-squared Estimation of Random Parameters
  18. Lesson 14 Maximum a Posteriori Estimation of Random Parameters
  19. Lesson 15 Elements of Discrete-time Gauss-Markov Random Sequences
  20. Lesson 16 State Estimation: Prediction
  21. Lesson 17 State Estimation: Filtering (the Kalman Filter)
  22. Lesson 18 State Estimation: Filtering Examples
  23. Lesson 19 State Estimation: Steady-state Kalman Filter and Its Relationship to a Digital Wiener Filter
  24. Lesson 20 State Estimation: Smoothing
  25. Lesson 21 State Estimation: Smoothing (General Results)
  26. Lesson 22 State Estimation for the Not-so-basic State-variable Model
  27. Lesson 23 Linearization and Discretization of Nonlinear Systems
  28. Lesson 24 Iterated Least Squares and Extended Kalman Filtering
  29. Lesson 25 Maximum-likelihood State and Parameter Estimation
  30. Lesson 26 Kalman-Bucy Filtering
  31. Lesson A Sufficient Statistics and Statistical Estimation of Parameters
  32. Lesson B Introduction to Higher-order Statistics
  33. Lesson C Estimation and Applications of Higher-order Statistics
  34. Lesson D Introduction to State-variable Models and Methods
  35. Appendix A Glossary of Major Results
  36. Appendix B Estimation Algorithm M-Files
  37. Appendix C Answers to Summary Questions
  38. References
  39. Index