Lessons in Estimation Theory for Signal

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	p. 1
The Linear Model	p. 9
Least-squares Estimation: Batch Processing	p. 27
Least-squares Estimation: Singular-value Decomposition	p. 44
Least-squares Estimation: Recursive Processing	p. 58
Small-sample Properties of Estimators	p. 74
Large-sample Properties of Estimators	p. 91
Properties of Least-squares Estimators	p. 108
Best Linear Unbiased Estimation	p. 121
Likelihood	p. 137
Maximum-likelihood Estimation	p. 147
Multivariate Gaussian Random Variables	p. 164
Mean-squared Estimation of Random Parameters	p. 173
Maximum a Posteriori Estimation of Random Parameters	p. 192
Elements of Discrete-time Gauss-Markov Random Sequences	p. 211
State Estimation: Prediction	p. 227
State Estimation: Filtering (the Kalman Filter)	p. 242
State Estimation: Filtering Examples	p. 259
State Estimation: Steady-state Kalman Filter and Its Relationship to a Digital Wiener Filter	p. 279
State Estimation: Smoothing	p. 304
State Estimation: Smoothing (General Results)	p. 317
State Estimation for the Not-so-basic State-variable Model	p. 345
Linearization and Discretization of Nonlinear Systems	p. 364
Iterated Least Squares and Extended Kalman Filtering	p. 384
Maximum-likelihood State and Parameter Estimation	p. 397
Kalman-Bucy Filtering	p. 413
A Sufficient Statistics and Statistical Estimation of Parameters	p. 436
B Introduction to Higher-order Statistics	p. 450
C Estimation and Applications of Higher-order Statistics	p. 473
D Introduction to State-variable Models and Methods	p. 499
App. A Glossary of Major Results	p. 518
App. B Estimation Algorithm M-Files	p. 524
App. C Answers to Summary Questions	p. 539
References	p. 542
Index	p. 553
Table of Contents provided by Blackwell. All Rights Reserved.

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

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