Electromagnetic situation analysis and judgment based on deep learning

• Main Authors: Yuntian Feng, Bing Li, Qibin Zheng, Dezheng Wang, Xiong Xu, Rongqing Zhang
• Format: Article
• Language: English
• Published: Wiley 2021-07-01
• Series: IET Communications
• Online Access: https://doi.org/10.1049/cmu2.12161

Abstract The electromagnetic situation, which can promote the abilities of understanding and decision‐making for the battlefield, has attracted significant interest recently in information‐based warfare. This paper investigates the deep learning‐based electromagnetic situation analysis and judgment in a complicated battlefield environment. To comprehensively simulate the two‐sided battling process, a turn‐based confrontation strategy is proposed, and an electromagnetic situation analysis and judgment model are then designed based on the AlphaGo Zero algorithm to achieve efficient situation analysis and decision‐making. In addition, an electromagnetic situation‐based attack‐defense platform is developed to realize and evaluate this designed model. Simulation results demonstrate that this designed model achieves significant performance in electromagnetic situation analysis and judgment compared with the Monte Carlo Tree Search based baseline.