Table of Contents

Probability and Random Variables: A Review. Mathematical Description of Random Signals. Response of Linear Systems to Random Inputs. Wiener Filtering. The Discrete Kalman Filter, State-Space Modeling, and Simulation. Prediction, Applications, and More Basics on Discrete Kalman Filtering. The Continuous Kalman Filter. Smoothing. Linearization and Additional Intermediate-Level Topics on Applied Kalman Filtering. More on Modeling: Integration of Noninertial Measurements Into INS. The Global Positioning System: A Case Study. Appendices. Index.