Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (2): 361-373.doi: 10.23919/JSEE.2023.000116

• SYSTEMS ENGINEERING • Previous Articles    

Aerial target threat assessment based on gated recurrent unit and self-attention mechanism

Chen CHEN1,2(), Wei QUAN1,2,*(), Zhuang SHAO1,2()   

  1. 1 School of Automation, Beijing Institute of Technology, Beijing 100081, China
    2 State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing 100081, China
  • Received:2022-08-17 Online:2024-04-18 Published:2024-04-18
  • Contact: Wei QUAN;;
  • About author:
    CHEN Chen was born in 1982. She received her Ph.D. degree from Beijing Institute of Technology. She is a professor in Beijing Institute of Technology. Her research interests are intelligent optimization and decision-making of complex systems, and military operations research. E-mail:

    QUAN Wei was born in 1998. He received his B.S. degree from Beijing Institute of Technology in 2021. He is currently a master degree candidate in Beijing Institute of Technology. His research interests are battlefield situation assessment and effectiveness evaluation. E-mail:

    SHAO Zhuang was born in 1997. He received his B.S. degree from Civil Aviation University of China in 2019. He received his M.S. degree from Beijing Institute of Technology in 2022. His research interests are battlefield situation understanding and machine learning. E-mail:
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (62022015; 62088101), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100), and Shanghai Municipal Commission of Science and Technology Project (19511132101).


Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit (SA-GRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform (FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features. Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

Key words: target threat assessment, gated recurrent unit (GRU), self-attention (SA), fractional Fourier transform (FRFT)