Stong Tracking Unscented Kalman Filter for Tracking A Ballistic Target Based on A Maximum Posterior Estimation
ZHANG Na-wen
WANG Yun
LIU Chang-yun
LI Shu-bin
ZHANG Chun-mei
Abstract：In order to solve the nonlinear tracking problems,a new nonlinear filter algorithm,i.e.stong tracking unscented kalman filter,on maximum posterior estimation is presented.The new algorithm a-dopts minimal skew simplex sampling strategy to reduce the computation time and insures the accuracy as well.Unexpected maneuvering is tracked stabely by using strong tracking filter to calculate single-step forecast mean square error.The recursive equations of time-varying noise statistic estimator are given through exponential weight of the constant noise statistic estimator to calculate statistical property of sys-tem condition noise.For this reason,the capability of dealing with variable noise statistic is improved.The simulation results show that the tracking performance of the new method is better than that of the un-scented kalman filter(UKF)and that of the extended kalman filter (EKF).
Keywords：minimal skew simplex samplingstong tracking filtermaximum posterior estimationexpo-nential weightunscented kalman filter
Publication Date：2014-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 25-29 )