Aiming at the problem of low accuracy of flight trajectory prediction caused
by the high speed of fighters, the diversity of tactical maneuvers, and the
transient nature of situational change in close range air combat, this paper
proposes an enhanced CNN-LSTM network as a fighter flight trajectory prediction
method. Firstly, we extract spatial features from fighter trajectory data using
CNN, aggregate spatial features of multiple fighters using the social-pooling
module to capture geographic information and positional relationships in the
trajectories, and use the attention mechanism to capture mutated trajectory
features in air combat; subsequently, we extract temporal features by using the
memory nature of LSTM to capture long-term temporal dependence in the
trajectories; and finally, we merge the temporal and spatial features to
predict the flight trajectories of enemy fighters. Extensive simulation
experiments verify that the proposed method improves the trajectory prediction
accuracy compared to the original CNN-LSTM method, with the improvements of 32%
and 34% in ADE and FDE indicators.

本文提出了一种增强的 CNN-LSTM 网络作为战斗机飞行轨迹预测方法，该方法从空间和时间两个维度提取特征，通过模拟实验证明相比于原始的 CNN-LSTM 方法，预测精度提高了 32% 和 34%。