Optimal Estimation of Dynamic Systems

Least Squares Approximation
A Curve Fitting Example
Linear Batch Estimation
Linear Sequential Estimation
Nonlinear Least Squares Estimation
Basis Functions
Advanced Topics

Probability Concepts in Least Squares
Minimum Variance Estimation
Unbiased Estimates
Maximum Likelihood Estimation
Cramer-Rao Inequality
Constrained Least Squares Covariance
Maximum Likelihood Estimation
Properties of Maximum Likelihood Estimation
Bayesian Estimation
Advanced Topics

Sequential State Estimation
A Simple First-Order Filter Example
Full-Order Estimators
The Discrete-Time Kalman Filter
The Continuous-Time Kalman Filter
The Continuous-Discrete Kalman Filter
Extended Kalman Filter
Unscented Filtering
Constrained Filtering

Advanced Topics in Sequential State Estimation
Factorization Methods
Colored-Noise Kalman Filtering
Consistency of the Kalman Filter
Consider Kalman Filtering
Decentralized Filtering
Adaptive Filtering
Ensemble Kalman Filtering
Nonlinear Stochastic Filtering Theory
Gaussian Sum Filtering
Particle Filtering
Error Analysis
Robust Filtering

Batch State Estimation
Fixed-Interval Smoothing
Fixed-Point Smoothing
Fixed-Lag Smoothing
Advanced Topics

Parameter Estimation: Applications
Attitude Determination
Global Positioning System Navigation
Simultaneous Localization and Mapping
Orbit Determination
Aircraft Parameter Identification
Eigensystem Realization Algorithm

Estimation of Dynamic Systems: Applications
Attitude Estimation
Inertial Navigation with GPS
Orbit Estimation
Target Tracking of Aircraft
Smoothing with the Eigensystem Realization Algorithm

Optimal Control and Estimation Theory
Calculus of Variations
Optimization with Differential Equation Constraints
Pontryagin’s Optimal Control Necessary Conditions
Discrete-Time Control
Linear Regulator Problems
Linear Quadratic-Gaussian Controllers
Loop Transfer Recovery
Spacecraft Control Design

Appendix A: Review of Dynamical Systems
Appendix B: Matrix Properties
Appendix C: Basic Probability Concepts
Appendix D: Parameter Optimization Methods
Appendix E: Computer Software

Index

A Summary appears at the end of each chapter.

Biography

John L. Crassidis, Ph.D., is a professor of mechanical and aerospace engineering and the associate director of the Center for Multisource Information Fusion at the University at Buffalo, State University of New York. He previously worked at Texas A&M University, the Catholic University of America, and NASA’s Goddard Space Flight Center, where he contributed to attitude determination and control schemes for numerous spacecraft missions.

John L. Junkins, Ph.D., is a distinguished professor of aerospace engineering and the founder and director of the Center for Mechanics and Control at Texas A&M University. In addition to his historical contributions in analytical dynamics and spacecraft GNC, Dr. Junkins and his team have designed, developed, and demonstrated several new electro-optical sensing technologies.

Praise for the First Edition
A nice feature of this book is that it makes the effort to explain the underlying principles behind the formula for each algorithm; the relationship between different algorithms is equally well addressed. … The text is a good combination of theory and practice. It will be a valuable addition to references for academic researchers and industrial engineers working in the field of estimation. It will also serve as a useful reference for graduate courses in control and estimation.
—AIAA Journal, Vol. 43, No. 1, January 2005