Kalman Filtering: Theory and Practice

1. General Information. 2. Linear Dynamic Systems. 3. Random Processes and Stochastic Systems. 4. Linear Optimal Filters and Predictors. 5. Nonlinear Applications. 6. Implementation Methods. 7. Practical Considerations. Appendix A: Software. Appendix B: A Matrix Refresher.