Kalman, H-Infinity, and Nonlinear Estimation Approaches
$1995 per person
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.
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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