A bottom-up approach that enables readers to master and apply the latest techniques in state estimationThis book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering.While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning:* Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation* Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice* MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parametersArmed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering.Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exerciseshelp readers apply theory to problems similar to ones they are likely to encounter in industry. A solutions manual is available for instructors.With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.A solutions manual is available upon request from the Wiley editorial board.
Author(s): Dan Simon
Edition: 1st edition
Publisher: Wiley-Interscience
Year: 2006
Language: English
Pages: 550
Cover Page......Page 1
Title Page......Page 3
ISBN 0471708585......Page 4
1 Linear systems theory......Page 5
4 Propagation of states and covariances......Page 6
7 Kalman filter generalizations......Page 7
9 Optimal smoothing......Page 8
12 Additional topics in H∞ filtering......Page 9
15 The particle filter......Page 10
Appendixes, References, Index......Page 11
Acknowledgments......Page 12
Acronyms......Page 13
List of algorithms (with page links)......Page 15
Introduction......Page 18
PART I INTRODUCTORY MATERIAL......Page 24
1 Linear systems theory......Page 26
2 Probability theory......Page 72
3 Least squares estimation......Page 102
4 Propagation of states and covariances......Page 130
PART II THE KALMAN FILTER......Page 144
5 The discretetime Kalman filter......Page 146
6 Alternate Kalman filter formulations......Page 172
7 Kalman filter generalizations......Page 206
8 The continuous-time Kalrnan filter......Page 252
9 Optimal smoothing......Page 286
10 Additional topics in Kalman filtering......Page 320
PART III THE H∞ FILTER......Page 354
11 The H∞ filter......Page 356
12 Additional topics in H∞ filtering......Page 396
PART IV NONLINEAR FILTERS......Page 416
13 Nonlinear Kalman filtering......Page 418
14 The unscented Kalman filter......Page 456
15 The particle filter......Page 484
Appendix A: Historical perspectives......Page 508
Appendix B: Other books on Kalman filtering......Page 512
Appendix C: State estimation and the meaning of life......Page 516
References......Page 524
A,B,C......Page 544
D,E,F,G,H,I,J,K......Page 545
L,M......Page 546
N,O,P......Page 547
Q,R,S......Page 548
T,U,V,W,Y......Page 549
Back Page......Page 550
