2nd Edition

Optimal Estimation of Dynamic Systems

By John L. Crassidis, John L. Junkins Copyright 2012
    750 Pages 117 B/W Illustrations
    by Chapman & Hall

    Optimal Estimation of Dynamic Systems, Second Edition highlights the importance of both physical and numerical modeling in solving dynamics-based estimation problems found in engineering systems. Accessible to engineering students, applied mathematicians, and practicing engineers, the text presents the central concepts and methods of optimal estimation theory and applies the methods to problems with varying degrees of analytical and numerical difficulty. Different approaches are often compared to show their absolute and relative utility. The authors also offer prototype algorithms to stimulate the development and proper use of efficient computer programs. MATLAB® codes for the examples are available on the book’s website.

    New to the Second Edition
    With more than 100 pages of new material, this reorganized edition expands upon the best-selling original to include comprehensive developments and updates. It incorporates new theoretical results, an entirely new chapter on advanced sequential state estimation, and additional examples and exercises.

    An ideal self-study guide for practicing engineers as well as senior undergraduate and beginning graduate students, the book introduces the fundamentals of estimation and helps newcomers to understand the relationships between the estimation and modeling of dynamical systems. It also illustrates the application of the theory to real-world situations, such as spacecraft attitude determination, GPS navigation, orbit determination, and aircraft tracking.

    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


    A Summary appears at the end of each chapter.


    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