针对现有的弹道落点预测方法误差大和气象变化适应不足的问题,建立了包含气象条件的弹道数据集,并提出了一种基于自注意力层的CNN-BiLSTM-BiGRU弹道落点预测方法。在所构建的组合模型中引入自注意力层和残差连接,加强模型在处理输入序列时动态关注不同时刻信息的能力,缓解网络中的梯度爆炸问题。采用多维时间序列数据的输入表示方法,结合历史弹道轨迹数据和目标特征等信息,减小弹道落点预测误差。仿真结果表明,基于自注意力层的CNN-BiLSTM-BiGRU网络模型的预测效果优于其他模型,射程预测的最大误差占真实值的0.156%,横偏预测的最大误差占真实值的5.904%。文中研究为弹道落点预测领域提供了重要的参考依据。

Aiming at the problems of large errors and insufficient adaptation to meteorological changes in existing ballistic drop prediction methods, a ballistic dataset containing meteorological conditions is established and a CNN-BiLSTM-BiGRU ballistic drop prediction method based on a self-attention layer is proposed in this paper. The self-attention layer and residual connection are introduced into the constructed combined model to strengthen the model's ability to dynamically focus on the information at different moments when processing the input sequences, and to alleviate problems such as gradient explosion in the network. It also uses the input representation of multi-dimensional time series data to reduce the ballistic drop prediction error by combining multiple information such as historical ballistic trajectory data and target characteristics. The simulation results show that the prediction effect of the CNN-BiLSTM-BiGRU network model based on the self-attention layer is better than the other models, and the maximum error of the range prediction accounts for 0.156% of the true value, and the maximum error of the lateral deviation prediction accounts for 5.904% as of the true value. The method provides an important reference for the field of ballistic drop prediction.