Data Fusion and State Estimation

3 Credits

A comprehensive course in data fusion and Kalman Filtering for optimal state estimation of dynamic systems. Includes coverage of basic probability, dynamic system modeling, state space representation of dynamic systems, state estimation theory, least squares estimation, and discrete Kalman filter estimation. This course implements the theory through extensive modeling and simulation using Python and C++. A design project provides experience with practical design issues and tradeoffs.