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| Air Defence and Anti-Missile Interception Decision-Making Study Based on Deep Learning |
| CUI Shan1， PAN Junyang2， WANG Wei1， GUO Ye1， XU Jiangtao2 |
| 1. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109， China； 2. College of Aerospace and Civil Engineering， Harbin Engineering University， Harbin 150001， Heilongjiang， China |
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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.
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Received: 22 July 2024
Published: 23 November 2024
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2024, Vol. 7 
