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Kalman filter is a minimum-variance estimation for dynamic systems and has attracted much attention with the increasing demands of target tracking. Various algorithms of Kalman filter was proposed for deriving optimal state estimation in the last thirty years. This paper briefly surveys the recent developments about Kalman filter (KF), Extended Kalman filter (EKF) and Unscented Kalman filter (UKF). The basic theories of Kalman filter are introduced, and the merits and demerits of them are analyzed and compared. Finally relevant conclusions and development trends are given.
Li et al. (Sun,) studied this question.