Research of Air Mission Recognition Method Based on Deep Learning

Authors

Qingkai Yao, Department of Information Warfare and Command Training National Defense University, Beijing 100091, China;
Shaojun Liu, Department of Information Warfare and Command Training National Defense University, Beijing 100091, China;
Xiaoyuan He, Department of Information Warfare and Command Training National Defense University, Beijing 100091, China;
Ou Wei, Department of Information Warfare and Command Training National Defense University, Beijing 100091, China;

Abstract

Abstract: In the large-scale simulation of war game, the air mission is the focus of the commander's attention. The rapid, accurate and automatic recognition of air missions is the prerequisite and basis for intelligent decision making. The rapid development of deep learning technology provided a practical and feasible solution for the extraction of complex battlefield posture features, and provided technical support for studying air mission recognition. The research progress of the traditional mission recognition research method and the mission recognition method based on the deep learning was summarized. The three methods of deep learning of Convolution Neural Network (CNN), Long-short Term Memory Network (LSTM) and Generate Adversarial Network (GAN) air mission recognition problem in the application were discussed, putting forward the solution ideas.

Recommended Citation

Yao, Qingkai; Liu, Shaojun; He, Xiaoyuan; and Wei, Ou (2020) "Research of Air Mission Recognition Method Based on Deep Learning," Journal of System Simulation: Vol. 29: Iss. 9, Article 48.
DOI: 10.16182/j.issn1004731x.joss.201709047
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss9/48

First Page

2227

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201709047

Last Page

2231

CLC

TP183

Recommended Citation

Yao Qingkai, Liu Shaojun, He Xiaoyuan, Ou Wei. Research of Air Mission Recognition Method Based on Deep Learning[J]. Journal of System Simulation, 2017, 29(9): 2227-2231.

DOI

10.16182/j.issn1004731x.joss.201709047