This study proposes an algorithm to calculate fast the predicted intercept point (PIP) of the anti-ballistic missile system. A neural network system is trained to learn the motion of the ballistic target to predict the future target position. Then a prediction algorithm iteratively calculates PIP and the launch time of the interceptor. PIP calculation enables the missile to effectively approach to the ballistic target. Computer simulations using simple interceptor models are conducted, demonstrating the usefulness of the proposed PIP determination algorithm. The proposed algorithm significantly reduces the computation time required for target trajectory prediction in real-time.

中文翻译：

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基于神经网络学习的弹道导弹防御拦截点预测

本研究提出了一种快速计算反弹道导弹系统预测拦截点（PIP）的算法。训练神经网络系统来学习弹道目标的运动以预测未来的目标位置。然后预测算法迭代计算PIP和拦截器的发射时间。PIP 计算使导弹能够有效逼近弹道目标。进行了使用简单拦截器模型的计算机模拟，证明了所提出的 PIP 确定算法的有用性。所提出的算法显着减少了实时目标轨迹预测所需的计算时间。