基于深度学习的空中目标威胁评估方法

针对空中目标威胁评估因素多、现有评估方法缺乏自学习能力的问题，采用深度学习理论建立了空中目标威胁评估的深层神经网络模型。为了提升模型训练的拟合效果，提出采用对称式的预训练方法，逐层地对模型中的隐含层进行预训练，最后对模型进行整体训练。分别通过样本测试集和空空仿真场景进行验证测试，结果表明：采用对称预训练方法，模型的威胁评估准确率高于其他三种预初始化方法；模型具有较好的鲁棒性，在无噪声下准确率大于90%,10%的正态噪声下，准确率大于70%。

Aerial Target Threat Assessment Method based on Deep Learning

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.