基于时空图注意力网络的战斗机飞行轨迹预测
Fighter flight trajectory prediction based on spatio-temporal graphcial attention network
AI总结：
针对近距空战中战斗机轨迹预测精度不足的问题，提出带编解码结构的时空图注意力网络ST-GAT，融合时空特征预测轨迹，在ADE和FDE指标上较ECNN-LSTM分别提升47%和34%。
摘要：
快速准确地预测近距空战中蓝军战斗机的飞行轨迹，有助于红军战斗机占据态势优势，这是后续空战获胜的关键因素。然而，由于先进战斗机具备高速甚至高超音速能力、战术机动具有多样性、态势转换具有瞬时性，现有方法在预测精度上难以满足实战应用需求。为提升预测精度，本文提出一种采用编解码结构的时空图注意力网络（ST-GAT，spatio-temporal graph attention network）用于飞行轨迹预测。编码器采用Transformer与GAT（图注意力网络）分支的并行结构，两个分支的前端均嵌入多头自注意力机制。Transformer分支网络用于提取历史轨迹的时间特征，捕捉战斗机历史状态对未来轨迹的影响；GAT分支网络用于提取历史轨迹中的空间特征，捕捉战斗机之间潜在的空间关联。随后我们将两个分支的输出拼接为新的特征向量，输入由全连接网络构成的解码器，预测蓝军战斗机的未来位置坐标。计算机仿真结果表明，与增强型CNN-LSTM网络（ECNN-LSTM）相比，本文提出的网络显著提升了飞行轨迹预测精度，ADE（平均位移误差）和FDE（最终位移误差）两项指标分别提升了47%和34%，为后续自主作战任务提供了有力支撑。
英文摘要：
Quickly and accurately predicting the flight trajectory of a blue army fighter in close-range air combat helps a red army fighter gain a dominant situation, which is the winning factor in later air combat. However,due to the high speed and even hypersonic capabilities of advanced fighters, the diversity of tactical maneuvers,and the instantaneous nature of situational transitions,it is difficult to meet the requirements of practical combat applications in terms of prediction accuracy.To improve prediction accuracy,this paper proposes a spatio-temporal graph attention network (ST-GAT) using encoding and decoding structures to predict the flight trajectory. The encoder adopts a parallel structure of Transformer and GAT branches embedded with the multi-head self-attention mechanism in each front end. The Transformer branch network is used to extract the temporal characteristics of historical trajectories and capture the impact of the fighter's historical state on future trajectories, while the GAT branch network is used to extract spatial features in historical trajectories and capture potential spatial correlations between fighters.Then we concatenate the outputs of the two branches into a new feature vector and input it into a decoder composed of a fully connected network to predict the future position coordinates of the blue army fighter.The computer simulation results show that the proposed network significantly improves the prediction accuracy of flight trajectories compared to the enhanced CNN-LSTM network (ECNN-LSTM), with improvements of 47% and 34% in both ADE and FDE indicators,providing strong support for subsequent autonomous combat missions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
