用于弹道目标跟踪的新的非线性滤波算法  

New nonlinear filtering algorithm for ballistic target tracking

作　　者：巫春玲[1] 韩崇昭[2] 

机构地区：[1]长安大学电子与控制工程学院,西安710064 [2]西安交通大学电子与信息工程学院,西安710049

出　　处：《计算机工程与应用》2011年第20期20-23,共4页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60574033);国家重点基础研究发展规划(973)(No.2007CB311006)~~

摘　　要：针对再入阶段弹道目标的跟踪问题,提出一种新的自适应滤波算法,即强跟踪有限差分扩展卡尔曼滤波(STFDEKF)算法,用于非线性系统的目标跟踪。该方法使用Sterling内插公式进行多项式的近似,从而实现对非线性函数的近似,避免了非线性函数的求导运算;并且算法中引入强跟踪的因子来修正先验的协方差矩阵。新算法改进了跟踪精度,扩大了应用范围,增强了滤波收敛性。仿真实验将新算法与扩展卡尔曼滤波器(EKF)、有限差分扩展卡尔曼滤波器(FDEKF)进行了比较,结果表明,STFDEKF在跟踪精度和滤波可靠性上均优于EKF和FDEKF,但其计算复杂性更大。得出结论,STFDEKF是个很有效的非线性滤波算法。This paper studies the problem of tracking a ballistic target in the reentry phase.It proposes an adaptive algorithm, Strong Tracking Finite-Difference Extended Kalman Filter（STFDEKF）,for ballistic target tracking in reentry.This method uses polynomial approximations obtained with a Sterling interpolation formula to approximate the derivative of the nonlinear function,and uses strong tracking factors to modify the prior covariance matrix.The proposed algorithm improves the tracking accuracy, enlarges the applied area and enhances the filtering convergence.It compares the performance of the proposed algorithm with that of the Extended Kalman Filter（EKF） and the Finite-Difference Extended Kalman Filter（FDEKF） using a Monte Carlo simulation.The simulation results show that STFDEKF outperforms EKF and FDEKF in terms of tracking accuracy, filter credibility and robustness against the sensitivity to filter initial condition.It concludes that the STFDEKF is an effective algorithm for the ballistic target tracking problem being studied.

关 键 词：弹道目标跟踪 扩展卡尔曼滤波 有限差分 强跟踪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]