"Kalman Filtering: Theory and Practice Using MATLAB" by Mohinder S. Grewal, Angus P. Andrews (Repost)

This book provides readers with a solid introduction to the theoretical and practical aspects of Kalman filtering. The book is designed to provide familiarity with both the theoretical and practical aspects of Kalman filtering by including real-world problems in practice as illustrative examples.

The material includes the essential technical background for Kalman filtering and the more practical aspects of implementation: how to represent the problem in a mathematical model, analyze the performance of the estimator as a function of system design parameters, implement the mechanization equations in numerically stable algorithms, assess its computational requirements, test the validity of results, and monitor the filter performance in operation.
The book has been updated with the developments in the implementation and application of Kalman filtering, including adaptations for nonlinear filtering, more robust smoothing methods, and developing applications in navigation.
All software is provided in MATLAB, giving readers the opportunity to discover how the Kalman filter works in action and to consider the practical arithmetic needed to preserve the accuracy of results.

Contents
Preface
1 General Information
2 Linear Dynamic Systems
3 Random Processes and Stochastic Systems
4 Linear Optimal Filters and Predictors
5 Optimal Smoothers
6 Implementation Methods
7 Nonlinear Filtering
8 Practical Considerations
9 Applications to Navigation
Appendix A: MATLAB Software
Appendix B: A Matrix Refresher
Bibliography
Index