关键词: 弹道模型, 深度学习, 监督学习, 卷积神经网络与长短期记忆神经网络模型, 轨迹预测

Abstract:

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 3s is about 14.83m in the x-axis direction, 20.77m in the y-axis direction and 0.75m 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.

Key words: ballistic model, deep learning, supervised learning, CNN-LSTM neural network model, trajectory prediction