基于 BiLSTM-Attention 和动态贝叶斯网络的防空目标智能意图预测方法

Aiming at the fact that the traditional battle intent prediction model cannot meet the requirements of prediction accuracy and reliability for air defense operations under system confrontation, an intelligent battle intent prediction method for air defense targets based on BiLSTM-Attention (bi-directional long short-term memory-attention) and dynamic Bayesian network is proposed. Considering target state information and target tactical information comprehensively, the combat intent feature set of the air defense target is designed; making full use of the collected historical moment data and predicted future moment data, the two-way loop and attention mechanism is introduced to simulate the reasoning process of the decision maker on the combat posture, highlighting the key information that affects the type of ballistic trajectory, and increasing the accuracy of the ballistic trajectory prediction of the air defense target. On this basis, a dynamic Bayesian network is constructed by combining the target trajectory, type, altitude and velocity to realize the accurate prediction of air defense target intent. The simulation experiments consider the effect of radar switch on target trajectory, and verify the feasibility and superiority of the proposed method under game confrontation conditions by comparing it with the traditional target intent prediction method.