Dan Simon

Optimal State Estimation (eBook, PDF)

Kalman, H Infinity, and Nonlinear Approaches

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 552
• Erscheinungstermin: 29. Juni 2006
• Englisch
• ISBN-13: 9780470045336
• Artikelnr.: 37290041

Autorenporträt

DAN SIMON, PhD, is an Associate Professor at Cleveland State University. Prior to this appointment, Dr. Simon spent fourteen years working for such firms as Boeing, TRW, and several smaller companies.

Inhaltsangabe

Acknowledgments. Acronyms. List of algorithms. Introduction. PART I
INTRODUCTORY MATERIAL. 1 Linear systems theory. 1.1 Matrix algebra and
matrix calculus. 1.2 Linear systems. 1.3 Nonlinear systems. 1.4
Discretization. 1.5 Simulation. 1.6 Stability. 1.7 Controllability and
observability. 1.8 Summary. Problems. Probability theory. 2.1 Probability.
2.2 Random variables. 2.3 Transformations of random variables. 2.4 Multiple
random variables. 2.5 Stochastic Processes. 2.6 White noise and colored
noise. 2.7 Simulating correlated noise. 2.8 Summary. Problems. 3 Least
squares estimation. 3.1 Estimation of a constant. 3.2 Weighted least
squares estimation. 3.3 Recursive least squares estimation. 3.4 Wiener
filtering. 3.5 Summary. Problems. 4 Propagation of states and covariances.
4.1 Discretetime systems. 4.2 Sampled-data systems. 4.3 Continuous-time
systems. 4.4 Summary. Problems. PART II THE KALMAN FILTER. 5 The
discrete-time Kalman filter. 5.1 Derivation of the discrete-time Kalman
filter. 5.2 Kalman filter properties. 5.3 One-step Kalman filter equations.
5.4 Alternate propagation of covariance. 5.5 Divergence issues. 5.6
Summary. Problems. 6 Alternate Kalman filter formulations. 6.1 Sequential
Kalman filtering. 6.2 Information filtering. 6.3 Square root filtering. 6.4
U-D filtering. 6.5 Summary. Problems. 7 Kalman filter generalizations. 7.1
Correlated process and measurement noise. 7.2 Colored process and
measurement noise. 7.3 Steady-state filtering. 7.4 Kalman filtering with
fading memory. 7.5 Constrained Kalman filtering. 7.6 Summary. Problems. 8
The continuous-time Kalman filter. 8.1 Discrete-time and continuous-time
white noise. 8.2 Derivation of the continuous-time Kalman filter. 8.3
Alternate solutions to the Riccati equation. 8.4 Generalizations of the
continuous-time filter. 8.5 The steady-state continuous-time Kalman filter
8.6 Summary. Problems. 9 Optimal smoothing. 9.1 An alternate form for the
Kalman filter. 9.2 Fixed-point smoothing. 9.3 Fixed-lag smoothing. 9.4
Fixed-interval smoothing. 9.5 Summary. Problems. 10 Additional topics in
Kalman filtering. 10.1 Verifying Kalman filter performance. 10.2
Multiple-model estimation. 10.3 Reduced-order Kalman filtering. 10.4 Robust
Kalman filtering. 10.5 Delayed measurements and synchronization errors.
10.6 Summary. Problems. PART I l l THE H, FILTER. 11 The H, filter. 11.1
Introduction. 11.2 Constrained optimization. 11.3 A game theory approach to
H, filtering. 11.4 The continuous-time H, filter. 11.5 Transfer function
approaches. 11.6 Summary. Problems. 12 Additional topics in H, filtering.
12.1 Mixed KalmanIH, filtering. 12.2 Robust Kalman/H, filtering. 12.3
Constrained H, filtering. 12.4 Summary. Problems. PART IV NONLINEAR
FILTERS. 13 Nonlinear Kalman filtering. 13.1 The linearized Kalman filter.
13.2 The extended Kalman filter. 13.3 Higher-order approaches. 13.4
Parameter estimation. 13.5 Summary. Problems. 14 The unscented Kalman
filter. 14.1 Means and covariances of nonlinear transformations. 14.2
Unscented transformations. 14.3 Unscented Kalman filtering. 14.4 Other
unscented transformations. 14.5 Summary. Problems. 15 The particle filter.
15.1 Bayesian state estimation. 15.2 Particle filtering. 15.3
Implementation issues. 15.4 Summary. Problems. Appendix A: Historical
perspectives. Appendix B: Other books on Kalman filtering. Appendix C:
State estimation and the meaning of life. References. Index.