基于LSTM与1DCNN的导弹轨迹预测方法

针对弹道导弹等超远程攻击目标的轨迹难以预测的问题，提出一种基于长短期记忆(long short-term memory, LSTM)网络与一维卷积神经网络(1-dimensional convolutional neural network, 1DCNN)的目标轨迹预测方法。首先，建立三自由度导弹运动模型，依据再入类型设计3种目标轨迹数据，构建机动数据库，解决轨迹数据的来源问题。其次，采用重复分割与滑动窗口的方法对轨迹数据进行预处理。然后，基于LSTM与1DCNN设计了一种目标类型分类网络，对目标进行初步分类。最后，基于1DCNN设计轨迹预测网络，对目标轨迹进行预测。仿真结果表明，提出的轨迹预测网络能够完成轨迹预测任务，预测误差在合理范围内。

Missile trajectory prediction method based on LSTM and 1DCNN

Aiming at the problem that it is difficult to predict the trajectory of ultra-long-range attack targets such as ballistic missiles, a target trajectory prediction method based on long short-term memory (LSTM)network and 1-dimensional convolutional neural network (1DCNN) is proposed. Firstly, a three-degree-of-freedom missile movement model is established, and three target trajectory data are designed according to the type of reentry, and a maneuvering database is constructed to solve the problem of the source of trajectory data. Secondly, the method of repeated segmentation and sliding window is used to preprocess the trajectory data. Then, a target type classification network is designed based on LSTM and 1DCNN to perform preliminary classification of targets. Finally, a trajectory prediction network is designed based on 1DCNN to predict the target trajectory. The simulation results show that the proposed trajectory prediction network can complete the trajectory prediction task, and the prediction error is within a reasonable range.