Includes bibliographical references (p. 549-564) and index.

Introduction

Linear Dynamic Systems

Probability and Expectancy

Random Processes

Linear Optimal Filters and Predictors

Optimal Smoothers

Implementation Methods

Nonlinear Approximations

Practical Considerations

Applications to Navigation

The definitive textbook and professional reference on Kalman Filtering - fully updated, revised, and expandedThis book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB®, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.