Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches

✍ Scribed by Dan Simon

• Publisher: Wiley-Interscience
• Year: 2006
• Tongue: English
• Leaves: 550
• Edition: 1
• Category: Library

✦ Synopsis

A bottom-up approach that enables readers to master and apply the latest techniques in state estimation

This 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 parameters

Armed 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 exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. 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.

✦ Table of Contents

Cover Page
Title Page
ISBN 0471708585
CONTENTS (with page links)
PART I INTRODUCTORY MATERIAL
1 Linear systems theory
2 Probability theory
3 Least squares estimation
4 Propagation of states and covariances
PART II THE KALMAN FILTER
5 The discretetime Kalman filter
6 Alternate Kalman filter formulations
7 Kalman filter generalizations
8 The continuous-time Kalrnan filter
9 Optimal smoothing
10 Additional topics in Kalman filtering



PART IV NONLINEAR FILTERS
13 Nonlinear Kalman filtering
14 The unscented Kalman filter
15 The particle filter
Appendixes, References, Index
Acknowledgments
Acronyms
List of algorithms (with page links)
Introduction
PART I INTRODUCTORY MATERIAL
1 Linear systems theory
2 Probability theory
3 Least squares estimation
4 Propagation of states and covariances
PART II THE KALMAN FILTER
5 The discretetime Kalman filter
6 Alternate Kalman filter formulations
7 Kalman filter generalizations
8 The continuous-time Kalrnan filter
9 Optimal smoothing
10 Additional topics in Kalman filtering



PART IV NONLINEAR FILTERS
13 Nonlinear Kalman filtering
14 The unscented Kalman filter
15 The particle filter
Appendix A: Historical perspectives
Appendix B: Other books on Kalman filtering
Appendix C: State estimation and the meaning of life
References
INDEX (with page links)
A,B,C
D,E,F,G,H,I,J,K
L,M
N,O,P
Q,R,S
T,U,V,W,Y
Back Page