S型机动条件下改进当前统计模型的跟踪算法

Research on Tracking Algorithms for Improving Current Statistical Model under S-Maneuvering Conditions

在高机动条件下,针对做S型机动的目标,提出了一种以当前统计模型为基础的卡尔曼滤波算法,并在当前统计模型的基础上,对算法进行了改进。通过实时调节“当前”统计模型最大加速度和修正卡尔曼滤波状态值的方法,使滤波结果有了一定程度的改善。仿真结果表明,两种改进算法都提高了全程滤波精度,尤其是提高了换向点处的收敛速度和跟踪精度。

Under the condition of high maneuverability,a kalman filtering algorithm based on the current statistical model is proposed for the S-type maneuvering target,which is improved on the basis of the traditionally current statistical model.By adjusting the maximum acceleration of the current statistical model and modifying the Kalman filter state value in real time,the filtering results are improved to a certain extent.The simulation results show that the two improved algorithms can improve the whole filtering accuracy,especially the convergence speed and tracking accuracy at the commutation point.