Kalman Filtering –Theory and Applications Intensive Course on Offered by Dr. Yeong-wei Andy wu Chief Engineer (retired) Flight and Control Directorate Space and Intelligence Systems, Boeing Company Boeing Senior Technical Fellow National Taiwan University of Science and Technology 3 credits certification will be given for accomplishing the course April 22~June 21, :15~21:05, each Tue., and Thu. Registration please contact Miss Tu, (02) or to For any query, please to or call (02) Prof. Shun-Feng The Kalman filter is probably the most successful and widely-used part of so-called "modern estimation theory“ since R.E. Kalman published his famous paper in 1960 describing a recursive solution to the discrete-data linear filtering problem. It has been used as the central piece of the algorithm for many applications in aircraft/ship/ground vehicle navigation, spacecraft attitude determination, orbit determination, missile guidance and control, RF antenna/laser terminal target acquisition/tracking, RF/optical signal acquisition and tracking, seismic data processing, medical signal processing, and other fields in the industry. This course is designed for graduate students and practitioners, such as system engineers, system analysts, software engineers, hardware engineers, and project managers, as well as military operational personnel who want to develop and/or enhance their knowledge and experiences in Kalman filters. It will provides a solid foundation for both the basic theory and practical application of Kalman filtering. Specific case studies are provided to illustrate the latter, including GPS navigation, integrated inertial navigation, precision navigation using GPS carrier-phase, spacecraft stellar-inertial attitude determination, spacecraft orbit determination, precision clock, and radar/laser target tracking. Lectures are further augmented by computer lab sessions using MATLAB to help participants develop insights through hands-on experience. Using the knowledge and skills gained through this course, students should be able to design Kalman filters for their specific fields; analyze the performance; develop the system, hardware, and software architecture; and resolve problems encountered in system integration, validation, and verification. Lecturer Summary: Dr. Wu has over 32 years of industry experience. He is a nationally recognized expert in the areas of spacecraft attitude determination and control; spacecraft antenna pointing and beam steering; airborne/space optical sensor Line-of-Sight (LOS) attitude determination and calibration; space cryogenic cooler vibration and temperature controls; transfer alignment for weapon delivery systems; and gimbal LOS pointing and stabilization. Dr. Wu had 52 issued US patents in the above fields and 51 papers published in AAS/AIAA/IEEE Conference Proceedings, AIAA Journal of GNC, and the Hughes Technical Journal. Dr. Wu has taught several technical courses including the Boeing Advanced Technical Education Program (ATEP) course on “Spacecraft Attitude Determination” and UCLA Extension courses on “Kalman Filtering – Theory and Applications” and “Spacecraft Attitude Control” Awards:  Recipient of 2005 Boeing Company Special Invention Awards  Recipient of 2003 Boeing Chief Technology Office Professional Excellence Award  Recipient of 2003 Asian American Engineer of the Year Award  Recipient of 2002 Boeing Company Special Invention Awards  Recipient of 2002 Boeing Satellite Systems Patent Award  Recipient of 2000 Boeing Satellite Systems Patent Award  Recipient of 1997 Hughes Space and Communication Technical Excellence Award  Recipient of 1994 Hughes Aircraft Company Patent Award

Kalman Filtering –Theory and Applications Intensive Course on National Taiwan University of Science and Technology 3 credits certification will be given for accomplishing the course April 22~June 21, :15~21:05, each Tue., and Thu. Registration please contact Miss Tu, (02) or to For any query, please to or call (02) Prof. Shun-Feng Course Syllabus 1)Reviews of mathematics ○Review of vectors & matrices ○Review of linear system theory Frequency domain representation Time domain representation ○Review of probability & random process theory 2)Basic estimation theory ○Deterministic estimation – least squared and weighted least squared estimates ○Probabilistic estimation – maximum likelihood and Baysian estimates ○Stochastic estimation – Wiener filter ○Kalman-Bucy filter 3)Kalman filter theory & practical application considerations ○Continuous Kalman filter: from discrete to continuous ○Special cases Correlated process and measurement noises Correlated measurement noises ○Solution of the Riccati equation; steady state Riccati equation ○Kalman-Bucy filter Orthogonal projection principle Wiener filter/Kalman-Bucy filter relationship ○Adaptive Kalman filter Use of innovation process Use of real-time parameter identification ○Kalman filter implementation considerations Modeling problems: filter divergence Filter divergence solutions −Estimate un-modeled states −Add process noise −Finite memory filtering −The Ɛ technique ○Constraints imposed by the computer Computation complexity Finite word length effect ○Numerical stability & alternative forms Joseph formulation Square root formulation UDU T covariance factorization formulation ○Kalman filter: architecture and algorithm 4)Case Studies ○Case study 1: Kalman filter for precision clock ○Case study 2: Kalman filter for Stellar Inertial Attitude Determination (SIAD) ○Case study 3: Kalman filter for RF Antenna Pointing/Acquisition/Tracking ○Case study 4: Kalman filter for Integrated Strap-Down Inertial Navigation 5)Computer Lab sessions ○1: Design & analysis of Kalman filter for precision clock ○2: Hands-On Lab Session on Bearing Only Tracking problems ○3: Hands-On Lab Session on 2D-GPS