The instructor presents state estimation theory clearly and rigorously, providing the right balance of fundamentals, advanced material, and recent research results. After taking this course, the student will be able to confidently apply state estimation techniques in a variety of fields. The features of this course include:
- A straightforward, bottom-up approach that begins with basic concepts, and then builds step-by-step to more advanced topics.
- Simple examples and problems that require paper and pencil to solve. This leads to an intuitive understanding of how theory works in practice.
- MATLAB®-based state estimation source code for realistic engineering problems. This enables students to recreate state estimation results and experiment with other simulation setups and parameters.
After being given a solid foundation in the fundmentals, students are presented with a careful treatment of advanced topics, including H-infinity filtering, unscented filtering, high-order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H-infinity filtering.
Who Should Attend
- Fundamentals (linear systems, probability, and least squares estimation)
- Kalman filtering and Kalman smoothing
- Kalman filter generalizations
- H-infinity filtering
- Nonlinear Kalman filtering
- Other nonlinear approaches: unscented Kalman filtering and particle filtering
This course will benefit engineers who want to estimate the states of a dynamic system. This includes electrical, aerospace, chemical, and mechanical engineers, but could also include engineers from other disciplines. This course will interest those who want to learn the basics, as well as those who want to learn about recent advances and research directions.