Abstract：In the scenario of complex naval escort missions, the current tactical decision support functions of anti-aircraft missile defense systems face issues such as high dependency on enemy models, poor accuracy in interception decisions, inability to effectively utilize historical battlefield data, and simplistic research objects. To resolve the above problems, a deep learning-based anti-missile interception intelligent decision-making model was proposed in this study. Firstly, a battlefield simulation platform was established to model the combat units accordingly. Then, an anti-missile interception intelligent decision-making model was designed using Long Short Term Memory neural networks. After that, a pre-battle model was trained using simulated data acquired from a constant proportional guidance particle model. Finally, the pre-battle model was transferred to the battlefield model and fine-tuned with real-time data enhanced with actual battlefield data through small-sample online training. Experiment results show that the proposed anti-missile interception intelligent decision-making model can effectively reduce dependency on enemy models and improve the accuracy of air defense missile decision-making.
崔闪, 潘俊杨, 王伟, 郭叶, 许江涛. 基于深度学习的防空反导拦截决策研究[J]. 空天防御, 2024, 7(5): 54-64.
CUI Shan, PAN Junyang, WANG Wei, GUO Ye, XU Jiangtao. Air Defence and Anti-Missile Interception Decision-Making Study Based on Deep Learning. Air & Space Defense, 2024, 7(5): 54-64.
