Optimal Estimation of Dynamic Systems: 2nd Edition (Hardback) book cover

Optimal Estimation of Dynamic Systems

2nd Edition

By John L. Crassidis, John L. Junkins

Chapman and Hall/CRC

749 pages | 117 B/W Illus.

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Description

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.

Reviews

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

Table of Contents

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.

About the Authors

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.

About the Series

Chapman & Hall/CRC Applied Mathematics & Nonlinear Science

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Subject Categories

BISAC Subject Codes/Headings:
MAT000000
MATHEMATICS / General
MAT003000
MATHEMATICS / Applied
TEC007000
TECHNOLOGY & ENGINEERING / Electrical
TEC009070
TECHNOLOGY & ENGINEERING / Mechanical