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UID:0-304@aerospace.technion.ac.il

DTSTART;TZID=Asia/Jerusalem:20200308T133000

DTEND;TZID=Asia/Jerusalem:20200308T143000

DTSTAMP:20230527T131446Z

URL:https://aerospace.technion.ac.il/events/the-meir-hanin-international-a
 erospace-prize-guest-seminar-the-challenges-of-nonlinear-kalman-filtering/

SUMMARY:The Meir Hanin International Aerospace Prize Guest Seminar - The Ch
 allenges of Nonlinear Kalman Filtering
DESCRIPTION:Lecturer:Prof. Mark L. Psiaki\n Faculty:Kevin T. Crofton Facult
 y Chair of Aerospace & Ocean Engineering\n Institute:Virginia Tech\, Black
 sburg\, VA\, U.S.A.\n Location:Auditorium 235\, Second Floor\, Aerospace E
 ng.\n Zoom: \n Abstract: \n Details: \n The challenges of nonlinear Kalman
  filtering are presented\, and a number of proposed solutions are examined
 . A Kalman filter forms an a posteriori estimate of the state of a dynamic
  system\, and it computes an estimation error covariance.  It provides th
 e optimal solution to any problem that is linear and Gaussian.  Various a
 pproximate Kalman filters and related estimation algorithms have been deve
 loped for nonlinear problems.  Some of them work well for important appli
 cations\, but none of them can provide a good solution to every conceivabl
 e nonlinear problem.  A given filter may diverge on certain problems.  A
 lternatively\, it may converge\, but yield much more estimation error than
  would an optimal filter.  Another filter may converge and achieve good a
 ccuracy\, but at a prohibitive cost in terms of computational resources. 
  The set of nonlinear filters that are reviewed and compared include the E
 xtended Kalman Filter (EKF)\, the Sigma-Points Filter -- also known as the
  Unscented Kalman Filter (UKF)\, the Particle Filter (PF)\, the Backward-S
 moothing Extended Kalman Filter (BSEKF) – a modification of the Moving H
 orizon Estimator (MHE)\, and the Gaussian Mixture Filter (GMF).  Nonlinea
 r problems that are used to examine these filters’ performance include s
 pacecraft attitude/rate/ moment-of-inertia estimation\, angles-only spacec
 raft orbit determination\, and the Blind Tricyclist nonlinear estimation b
 enchmark problem.  The filters are compared in terms of convergence relia
 bility\, accuracy\, and computational cost.  These comparisons demonstrat
 e that no single nonlinear filtering algorithm is superior to all others i
 n every respect.  Therefore\, a good estimation practitioner must know ab
 out many or most of these filtering options and choose among them to devel
 op the best solution to a given problem.
CATEGORIES:Seminars
LOCATION:Auditorium 235\, Second Floor\, Aerospace Eng.

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DTSTART:20191027T010000

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