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

ISBN-10: 0131209817
ISBN-13: 9780131209817
Edition: 2nd 1995
Authors: Jerry M. Mendel
List price: $116.00 Buy it from $59.03
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Description: Estimation theory is widely used in many branches of science and engineering. Written in a “lesson” format that is especially convenient for self-study, this book describes many of the important estimation methods and shows how they are  More...

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Book details

List price: $116.00
Edition: 2nd
Copyright year: 1995
Publisher: Prentice Hall PTR
Publication date: 3/14/1995
Binding: Hardcover
Pages: 592
Size: 7.25" wide x 9.75" long x 1.25" tall
Weight: 2.288
Language: English

Estimation theory is widely used in many branches of science and engineering. Written in a “lesson” format that is especially convenient for self-study, this book describes many of the important estimation methods and shows how they are interrelated.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. 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.For engineers and scientists interested in digital estimation theory.

Preface
Introduction, Coverage, Philosophy, and Computation
The Linear Model
Least-squares Estimation: Batch Processing
Least-squares Estimation: Singular-value Decomposition
Least-squares Estimation: Recursive Processing
Small-sample Properties of Estimators
Large-sample Properties of Estimators
Properties of Least-squares Estimators
Best Linear Unbiased Estimation
Likelihood
Maximum-likelihood Estimation
Multivariate Gaussian Random Variables
Mean-squared Estimation of Random Parameters
Maximum a Posteriori Estimation of Random Parameters
Elements of Discrete-time Gauss-Markov Random Sequences
State Estimation: Prediction
State Estimation: Filtering (the Kalman Filter)
State Estimation: Filtering Examples
State Estimation: Steady-state Kalman Filter and Its Relationship to a Digital Wiener Filter
State Estimation: Smoothing
State Estimation: Smoothing (General Results)
State Estimation for the Not-so-basic State-variable Model
Linearization and Discretization of Nonlinear Systems
Iterated Least Squares and Extended Kalman Filtering
Maximum-likelihood State and Parameter Estimation
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
App. A Glossary of Major Results
App. B Estimation Algorithm M-Files
App. C Answers to Summary Questions
References
Index

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