火力与指挥控制2024，Vol.49Issue(7)：64-69,6.DOI:10.3969/j.issn.1002-0640.2024.07.010
小样本下基于CNN-GRU网络的弹丸落点预测
Projectile Impact Point Prediction Based on CNN-GRU Network under Small Samples
王现磊 1陈铎 1薛景元 1王义江1
作者信息
- 1. 解放军63861部队,吉林 白城 137001
- 折叠
摘要
Abstract
In order to fully explore the law of projectile radial velocity in time and space,and im-prove the accuracy of projectile impact-point prediction,a method of projectile impact point prediction based on CNN-GRU is proposed.CNN and GRU networks are used respectively to extract the strong correlation characteristics of the projectile radial velocity in time and space,to learn the highly complex nonlinear flight trajectory,and to build the prediction model of projectile impact points.The radial ve-locity data of a certain type of projectile is used as the training set and test set to predict the impact points,and compared with the time series prediction methods of MLP,LSTM and CNN-LSTM.The ex-perimental results show that the CNN-GRU prediction model can effectively extract the spatiotemporal information in the projectile radial velocity sequence,and learn the position of the projectile relative to the radar.The comparisons with other models show that the predication model has higher prediction ac-curacy,faster convergence speed and better stability.关键词
径向速度/落点预测/卷积神经网络/门控循环单元Key words
radial velocity/impact-point prediction/convolutional neural network/gated recurrent unit分类
军事科技引用本文复制引用
王现磊,陈铎,薛景元,王义江..小样本下基于CNN-GRU网络的弹丸落点预测[J].火力与指挥控制,2024,49(7):64-69,6.
