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当坦克和目标进行机动时,坦克火炮射击解命中问题求解条件发生了巨大变化,分析表明,过去基于目标作匀速运动的假定使得求解解命中问题误差过大,以致很难命中目标。针对坦克及目标的机动特性,提出了增加车体运动速度传感器补偿弹丸附加初速、采用机动运动模型进行目标跟踪与预测等改进方案。并以“当前”统计模型为例,将目标机动和极坐标系引入的伪加速度一并考虑,直接在极坐标系下设计卡尔曼滤波算法用于解命中计算,在保证计算精度的情况下,减少了部分计算量。仿真结果证明了该方案的有效性。
When the tank and the target are maneuvering, the conditions for solving the shooting problem of the tank artillery firearound have undergone tremendous changes. The analysis shows that in the past, the assumption of constant motion based on the target made the error of the solution hit problem so large that it was very hard to hit the target. Aiming at the maneuvering characteristics of tanks and targets, some improved schemes are proposed, such as adding additional muzzle velocities by compensating the projectile velocity of the vehicle body motion velocity sensor and tracking and predicting the target by using the maneuvering motion model. Taking the “current” statistical model as an example, the pseudo-accelerations introduced by the target maneuvering and the polar coordinate system are considered together. The Kalman filter algorithm is designed directly to solve the hit calculation in the polar coordinate system. When the calculation accuracy is guaranteed Under, reduce the amount of computation. Simulation results show the effectiveness of this scheme.