该文研究了弹道系数未知的弹道再入目标的跟踪问题。针对现有再入目标跟踪方法对弹道系数初值设定以及噪声协方差的设置比较敏感的问题,采用了数值精度高、稳定性好且计算量较小的求容积卡尔曼滤波(CKF)算法作为跟踪滤波器,并分别设计了由不同弹道系数模型构成的交互式多模型(IMM)算法以及由不同噪声协方差模型组成的IMM算法。仿真结果表明,该文设计的两种IMM算法都能显著提高跟踪精度以及对弹道系数估计的收敛速度。

The tracking of ballistic reentry target with unknown ballistic coefficient was studied in this paper. In view of the problems that the existing reentry target tracking method was both sensitive to the ballistic coefficient initialization and the noise covariance setting, the cubature kalman filter (CKF) algorithm which has high numerical accuracy, good stability and small computation quantity was adopted as filtering, then the IMM algorithms composed of models with different ballistic coefficient and different noise covariance were designed respectively. Computer simulation results showed that the two IMM algorithms designed in this paper could significantly improve the tracking accuracy and the convergence rate of the ballistic coefficient estimation.