Journal of System Simulation
Abstract
Abstract: Due to many factors of aerial target threat assessment and the lack of self-learning ability of current assessment methods, a deep neural network model for aerial target threat assessment is established using deep learning theory. In order to improve the fitting effect of the model training, a symmetric pre-training method is given. The hidden layers of the model are pre-trained layer by layer, and finally the whole model is trained. Sample data and air to air simulation scene experiments are carried out respectively. The experiments results show that the accuracy of the model using the symmetric pre-training method is higher than the other three initialization methods. The accuracy of the model is more than 90% without noise and more than 70% under 10% normal noise, which shows its better robustness.
Recommended Citation
Chai, Huimin; Zhang, Yong; Li, Xinyue; and Song, Yanan
(2022)
"Aerial Target Threat Assessment Method based on Deep Learning,"
Journal of System Simulation: Vol. 34:
Iss.
7, Article 8.
DOI: 10.16182/j.issn1004731x.joss.21-0080
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss7/8
First Page
1459
Revised Date
2021-04-01
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.21-0080
Last Page
1467
CLC
TP391.9
Recommended Citation
Huimin Chai, Yong Zhang, Xinyue Li, Yanan Song. Aerial Target Threat Assessment Method based on Deep Learning[J]. Journal of System Simulation, 2022, 34(7): 1459-1467.
DOI
10.16182/j.issn1004731x.joss.21-0080
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons