Aerial Target Threat Assessment Method based on Deep Learning

doi:10.16182/j.issn1004731x.joss.21-0080

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.

Key words: aerial target, threat assessment, deep learning, symmetrical pre-training

CLC Number:

TP391.9

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.

Figures/Tables 10

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参数	值
输入层神经元个数	4
隐含层层数	5
各隐含层神经元个数	第一层隐含层12
	第二层隐含层11
	第三层隐含层10
	第四层隐含层9
	第五层隐含层25
输出层神经元个数	8
隐含层/输出层激活函数	Sigmoid

参数	值
训练样本总数量	18 000
最大迭代次数	600
每个batch中训练样本个数	100
测试样本数量	2 000

模型参数初始化方法	威胁等级准确率
本文	96.7
1	35.6
2	36.0
3	35.9

测试样本集	威胁等级准确率
未加噪声	96.7
加入5%正态噪声	90.1
加入10%正态噪声	73.5
加入15%正态噪声	61.2

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