In this work, we propose a novel missile guidance algorithm that combines deep learning based trajectory prediction with nonlinear model predictive control. Although missile guidance and threat interception is a well-studied problem, existing algorithms' performance degrades significantly when the target is pulling high acceleration attack maneuvers while rapidly changing its direction. We argue that since most threats execute similar attack maneuvers, these nonlinear trajectory patterns can be processed with modern machine learning methods to build high accuracy trajectory prediction algorithms. We train a long short-term memory network (LSTM) based on a class of simulated structured agile attack patterns, then combine this predictor with quadratic programming based nonlinear model predictive control (NMPC). Our method, named nonlinear model based predictive control with target acceleration predictions (NMPC-TAP), significantly outperforms compared approaches in terms of miss distance, for the scenarios where the target/threat is executing agile maneuvers.

翻译：在这项工作中,我们提出了一个新型的导弹指导算法,将基于深学习的轨迹预测与非线性模型预测控制结合起来。 尽管导弹指导和威胁拦截是一个研究周密的问题,但当目标正在拉动高速攻击动作同时迅速改变其方向时,现有算法的性能将显著下降。 我们争论说,由于大多数威胁都使用类似的攻击动作,这些非线性轨道模式可以用现代机器学习方法处理,以建立高精准轨迹预测算法。 我们根据模拟结构灵活的攻击模式来训练一个长期的短期记忆网络(LSTM ),然后将这一预测器与基于非线性模型预测的控制(NPC ) 。 我们的方法,即以目标加速预测为基础的非线性模型预测控制(NPC-TAP ),大大超出距离比方的方法,因为目标/威胁是执行灵活机动的。