## 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

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

## Product information

- Title: Lessons in Estimation Theory for Signal Processing, Communications, and Control, Second Edition
- Author(s):
- Release date: March 1995
- Publisher(s): Pearson
- ISBN: 9780132442206

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