空军工程大学学报（自然科学版）Issue(5)：25-29,5.DOI:10.3969/j.issn.1009-3516.2014.05.006
基于极大后验估计的STUKF算法跟踪再入弹道目标
Stong Tracking Unscented Kalman Filter for Tracking A Ballistic Target Based on A Maximum Posterior Estimation
摘要
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).关键词
最小偏度单行采样/强跟踪滤波器/极大后验估计/指数加权/不敏卡尔曼滤波Key words
minimal skew simplex sampling/stong tracking filter/maximum posterior estimation/expo-nential weight/unscented kalman filter分类
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张纳温,汪云,刘昌云,李树彬,张春梅..基于极大后验估计的STUKF算法跟踪再入弹道目标[J].空军工程大学学报（自然科学版）,2014,(5):25-29,5.基金项目
国家自然科学基金资助项目(61102109) （61102109）
