The target's threat prediction is an essential procedure for the situation analysis in an aerial defense system. However, the traditional threat prediction methods mostly ignore the effect of commander's emotion. They only predict a target's present threat from the target's features itself, which leads to their poor ability in a complex situation. To aerial targets, this paper proposes a method for its potential threat prediction considering commander emotion (PTP-CE) that uses the Bi-directional LSTM (BiLSTM) network and the backpropagation neural network (BP) optimized by the sparrow search algorithm (SSA). Furthermore, we use the BiLSTM to predict the target's future state from real-time series data, and then adopt the SSA-BP to combine the target's state with the commander's emotion to establish a threat prediction model. Therefore, the target's potential threat level can be obtained by this threat prediction model from the predicted future state and the recognized emotion. The experimental results show that the PTP-CE is efficient for aerial target's state prediction and threat prediction, regardless of commander's emotional effect.

目标威胁预测是防空系统态势分析的重要过程。然而，传统的威胁预测方法大多忽略了指挥官情绪的影响。他们只从目标本身的特征来预测目标当前的威胁，这导致他们在复杂情况下的能力很差。针对空中目标，本文提出了一种考虑指挥官情绪的潜在威胁预测方法（PTP-CE），该方法使用双向LSTM（BiLSTM）网络和麻雀搜索算法（SSA）优化的反向传播神经网络（BP）。 . 此外，我们使用 BiLSTM 从实时序列数据中预测目标的未来状态，然后采用 SSA-BP 将目标的状态与指挥官的状态相结合' s 情绪建立威胁预测模型。因此，该威胁预测模型可以从预测的未来状态和识别的情绪中获得目标的潜在威胁等级。实验结果表明，PTP-CE对于空中目标的状态预测和威胁预测是有效的，无论指挥官的情绪影响如何。