基于卷积神经网络与长短期记忆神经网络的弹丸轨迹预测

Projectile Trajectory Prediction Based on CNN-LSTM Model

针对弹丸非线性轨迹预测问题,提出一种基于卷积神经网络(CNN)与长短期记忆(LSTM)神经网络的混合轨迹预测模型。通过建立6自由度弹丸运动模型,并使用4阶龙格库塔法外弹道仿真,得到大量轨迹数据样本;提出CNN-LSTM神经网络的混合轨迹预测模型,并利用滑动窗口法和差分法构造输入输出的轨迹数据对,将预测问题转化为有监督的学习问题;将所提模型与LSTM神经网络模型、门控循环单元(GRU)神经网络模型和反向传播(BP)神经网络模型在同一数据集下进行仿真实验。研究结果表明,CNN-LSTM神经网络模型预测3 s后的平均累积预测误差在x轴方向约为14.83 m,y轴方向约为20.77 m,z轴方向约为0.75 m,且轨迹预测精度优于单一模型,为弹丸轨迹预测研究提供了一定的参考。

To solve the problem of nonlinear trajectory prediction of projectile,a novel hybrid trajectory prediction model based on convolutional neural network(CNN)and long and short-term memory(LSTM)neural network is proposed.A 6DOF projectile movement model is established,and a substantial dataset of trajectory samples is obtained through exterior ballistics simulations employing the four-order Runge-Kutta method.Secondly,the hybrid CNN-LSTM trajectory prediction model is proposed,and the input and output trajectory data pairs are constructed by using the sliding window method and first-order difference method,which transforms the prediction problem into a supervised learning problem.Then,the proposed model is compared with LSTM neural network model,gated recurrent unit(GRU)neural network model and back propagation(BP)neural network model using the same dataset.The results show that the average cumulative prediction error of CNN-LSTM model after 3 s is about 14.83 m in the x-axis direction,20.77 m in the y-axis direction and 0.75 m in the z-axis direction.The trajectory prediction accuracy of CNN-LSTM neural network model is better than that of a single model,which provides valuable insights for advancing projectile trajectory prediction research.