Kalman Filtering

• Representing the problem in a mathematical model
• Analyzing estimator performance as a function of model parameters
• Implementing the mechanization equations in numerically stable algorithms
• Assessing computational requirements
• Testing the validity of results
• Monitoring filter performance in operation

As the best way to understand and master a technology is to observe it in action, Kalman Filtering: Theory and Practice Using MATLAB®, Second Edition includes companion software in MATLAB®, providing users with an opportunity to experience first hand the filter’s workings and its limitations.

This updated and revised edition of Grewal and Andrews’s classic guide is an indispensable working resource for engineers and computer scientists involved in the design of aerospace and aeronautical systems, global positioning and radar tracking systems, power systems, and biomedical instrumentation.

Contents

• Preface.
• Acknowledgments.
• General Information.
• Linear Dynamic Systems.
• Random Processes and Stochastic Systems.
• Linear Optimal Filters and Predictors.
• Nonlinear Applications.
• Implementation Methods.
• Practical Considerations.
• Appendix A: MATLAB Software.
• Appendix B: A Matrix Refresher.
• References.
• Index.