【摘要】 针对巡飞弹和微型无人机对目标进行毁伤时，易受到炸点的时空位置和对目标时空位置探测精确度等因素的影响，使得其难以对目标造成有效毁伤的问题，提出了基于概率卡尔曼滤波和弹目交汇毁伤模型的状态重构级联模型。该方法将概率选择与卡尔曼滤波结合起来，能够依据前序时刻状态信息和系统模型实现对后续时刻状态的概率推断，从而对后续时刻目标的时空位置实现精度更高的预测。为检验所提出的状态估计和炸点控制算法，在真实的巡飞弹工况场景下开展了仿真实验，仿真分析表明该状态重构方法对弹目距离信息的估计误差较之传统的卡尔曼滤波和中值滤波降低了25%左右，且毁伤效能均在96%以上，并通过大量仿真分析验证了该方法在变化工况下具有良好的性能鲁棒性。更多还原

【Abstract】 For cruise missile and miniature drones in the target damage, which is vulnerable to time and space position and the target position detection precision and other factors, which makes it difficult to cause effective damage to the target, a method based on the probability Kalman filter and bullet intersection destruction model state reconstruction cascade model was put forward. This method combined probability selection with Kalman filter, and could realize the probability inference of the subsequent moment state based on the previous moment state information and the system model, so as to achieve higher accuracy prediction of the spatiotemporal position of the target in the subsequent moment. In order to test the proposed state estimation and blast point control algorithm, the simulation experiment was carried out in the real missile patrol condition scenario, which verified the improvement effect and the performance gain. The results showed that the algorithm could effectively improve the damage efficiency and had a strong robustness. Finally, the simulation analysis showed that the estimation error of the state reconstruction method on the bullet distance information was reduced by about 25% compared with the traditional Kalman filter and median filter, and the damage efficiency was more than 96%. Through a lot of simulation analysis, the method had good robustness under changing conditions.更多还原