An Air Combat Decision Learning System Based on a Brain-Like Cognitive Mechanism

  • Kai ZhouEmail author
  • Ruixuan Wei
  • Zhuofan Xu
  • Qirui Zhang
  • Hongxuan Lu
  • Guofeng Zhang


The unmanned aerial vehicle (UAV) has emerged unexpectedly as a new force in the recent local wars. To occupy the high ground of military technology in the future, research on autonomous air combat using UAVs is extremely important. In this paper, we propose an intelligent air combat learning system inspired by the cognitive mechanism of the human brain. The air combat capability was divided into two parts: declarative and procedural memory. Imitating the updating and storage mechanism of human knowledge, a long- and short-term hierarchical asynchronous learning principle was designed. We adopted the basic idea of using the error signal to drive learning according to neurophysiological research. Drawing lessons from the working memory mechanism, the error signal for driving the short-term learning was created in the absence of labelled data, using only the interactive information. Then, we proved that the learning mechanism could ensure that system performance advances steadily and gradually. The experiments illustrated that the learning system designed in this paper can achieve some inferior confrontation ability through self-learning without human prior knowledge. Action strategies formed by learning are analogous to the classical tactical manoeuvres of human fighter pilots. We compared our work with related works and found that our method could improve its performance continuously and finally defeat its opponent.


Autonomous air combat Learning system Brain-like cognitive mechanism Knowledge Multi-level memory Biological neural mechanism 


Funding Information

This study was funded by the National Natural Science Foundation of China (61573373).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Air Force Engineering UniversityXi’anChina

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