Kalman, H-Infinity, and Nonlinear Estimation Approaches
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This three-day course will introduce Kalman filtering and other state estimation algorithms in a practical way so that the student can design and apply state estimation algorithms for real problems. The course will also present enough theoretical background to justify the techniques and provide a foundation for advanced research and implementation. After taking this course the student will be able to design Kalman filters, H-infinity filters, and particle filters for both linear and nonlinear systems. The student will be able to evaluate the tradeoffs between different types of estimators. The algorithms will be demonstrated with freely available MATLAB programs. Each student will receive a copy of Dr. Simon’s text, Optimal State Estimation. It is beneficial –but not required- to bring a laptop loaded with MATLAB to this class.
What You Will Learn:
- How can I create a system model in a form that is amenable to state estimation?
- What are some different ways to simulate a system?
- How can I design a Kalman filter?
- What if the Kalman filter assumptions are not satisfied?
- How can I design a Kalman filter for a nonlinear system?
- How can I design a filter that is robust to model uncertainty?
- What are some other types of estimators that may do better than a Kalman filter?
- What are the latest research directions in state estimation theory and practice?
- What are the tradeoffs between Kalman, H-infinity, and particle filters?
- Dynamic Systems Review. Linear systems. Nonlinear systems. Discretization. System simulation.
- Random Processes Review. Probability. Random variables. Stochastic processes. White noise and colored noise.
- Least Squares Estimation. Weighted least squares. Recursive least squares.
- Time Propagation of States and Covariances.
- The Discrete Time Kalman Filter. Derivation. Kalman filter properties.
- Alternate Kalman filter forms. Sequential filtering. Information filtering. Square root filtering.
- Kalman Filter Generalizations. Correlated noise. Colored noise. Steady-state filtering. Stability. Alpha-beta-gamma filtering. Fading memory filtering. Constrained filtering.
- Optimal Smoothing. Fixed point smoothing. Fixed lag smoothing. Fixed interval smoothing.
- Advanced Topics in Kalman Filtering. Verification of performance. Multiple-model estimation. Reduced-order estimation. Robust Kalman filtering. Synchronization errors.
- H-infinity Filtering. Derivation. Examples. Tradeoffs with Kalman filtering.
- Nonlinear Kalman Filtering. The linearized Kalman filter. The extended Kalman filter. Higher order approaches. Parameter estimation.
- The Unscented Kalman Filter. Advantages. Derivation. Examples.
Dr. Dan Simon has been a professor at Cleveland State University since 1999 and is also the owner of Innovatia Software, an independent consulting firm. He had 14 years of industrial experience in the aerospace, automotive, biomedical, process control, and software engineering fields before entering academia. He has applied Kalman filtering and other state estimation techniques to a variety of areas, including motor control, neural network and fuzzy system optimization, missile guidance, communication networks, fault diagnosis, vehicle navigation, robotics, prosthetics, and financial forecasting. He has over 100 publications in refereed journals and conference proceedings, including many on the topic of Kalman filtering. He has written three graduate-level text books.
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