基于深度置信网络的近距空战态势评估

张彬超; 寇雅楠; 邬蒙; 左家亮

doi:10.13700/j.bh.1001-5965.2016.0956

基于深度置信网络的近距空战态势评估

doi: 10.13700/j.bh.1001-5965.2016.0956

空军工程大学航空航天工程学院, 西安 710038

基金项目:

航空科学基金 20155896026

详细信息

作者简介:
张彬超男, 硕士研究生。主要研究方向:机器学习与智能空战

寇雅楠女, 博士, 副教授, 硕士生导师。主要研究方向:航空兵作战训练与效能评估

通讯作者:
寇雅楠, E-mail:49841256@qq.com

中图分类号: V271;TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2016-12-20
- 录用日期: 2017-02-15
- 网络出版日期: 2017-07-20

Close-range air combat situation assessment using deep belief network

College of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an 710038, China

Funds:

Aeronautical Science Foundation of China 20155896026

More Information

Corresponding author: KOU Yanan, E-mail: 49841256@qq.com

摘要

摘要:
针对传统态势评估方法权值确定困难、大规模数据处理和特征提取能力不足的问题，结合当前空战数据特征，将深度置信网络（DBN）应用于近距空战态势评估。通过密度峰值算法对空战特征数据进行聚类分析，并结合态势函数和专家判读进行修正，建立标准空战态势样本库；以重构误差和测试错误率为基础，建立网络拓扑结构和最优参数确定方法，提高模型的训练效率，并通过样本数据，对模型进行训练和验证。实验表明，模型态势分类正确率达到92.7%，模型运行时间满足应用需求，实例评估结果与客观态势一致性强。
- 深度置信网络（DBN） /
- 态势评估 /
- 半监督学习 /
- 网络拓扑结构 /
- 密度峰值聚类
Abstract:
Considering the difficulty in parameter setting, weakness of traditional situation assessment methods in processing and feature extraction of big data, feature of air combat data, applications of deep belief network (DBN) to close-range air combat situation assessment are discussed. A sample library of combat situation was constructed. The data were clustered using density peaks algorithm, and the results were revised by specialists of air combat and traditional functions. Then the model of deep belief network was constructed. According to the standard of test and reconstruction error, the network topology structure and optimal parameters were determined. The model was trained by the data from the sample library. Experimental results show that the model's situation classification accuracy reaches to 92.7%, and its running time meets the application requirements. Analysis of the practical example verified the feasibility of the DBN model.
- deep belief network (DBN) /
- situation assessment /
- semi-supervised learning /
- network topology structure /
- density peaks clustering

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图 1 空战双方位置关系

Figure 1. Position relationship between two sides in air combat

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图 2 4种典型空战态势

Figure 2. Four kinds of typical air combat situations

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图 3 样本库构建流程图

Figure 3. Flowchart for constructing sample library

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图 4 密度峰值算法结果

Figure 4. Results of density peaks algorithm

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图 5 4类空战态势聚类结果

Figure 5. Clustering results of four kinds of air combat situations

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图 6 2种方法结果对比

Figure 6. Result comparison of two algorithms

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图 7 DBN结构

Figure 7. Structure of DBN

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图 8 RBM网络结构图

Figure 8. Structure of RBM network

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图 9 DBN深度确定流程图

Figure 9. Flowchart for determining depth of DBN

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图 10 不同深度DBN重构误差

Figure 10. Reconstruction error of different depth of DBN

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图 11 最后一层隐含层训练重构误差

Figure 11. Training reconstruction error of last hidden layer

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图 12 最后一层隐含层测试错误率

Figure 12. Test error rate of last hidden layer

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图 13 微调次数与测试错误率

Figure 13. Fine-tuning times and test error rate

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图 14 4类空战态势逐层特征提取线

Figure 14. Feature extraction lines per layer of four kinds of air combat situations

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图 15 两机空战对抗实例

Figure 15. Example of 1 versus 1 in air combat

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表 1 空战参数

Table 1. Parameters of air combat

参数	值域
目标方位角λ/rad	[0, π]
目标进入角ψ/rad	[0, π]
速度矢量夹角η/rad	[0, π]
两机距离r/km	[0, 10]
两机速度平方差Δv²
我机速度v_R
两机高度差Δh/m	[0, 18 000]
我机高度h/m	[0, 18 000]

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表 2 不同深度训练结果

Table 2. Training result of different depth

网络深度	测试错误率/%	训练时间/s
1	10.12	266.758 7
2	9.46	431.971 7
3	8.72	578.341 0
4	8.40	933.181 1
5	9.88	1 269.745 0

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表 3 3种算法效果对比

Table 3. Effect comparison of three algorithms

算法	训练集正确率/%(正确样本/训练样本)	测试集正确率/%(正确样本/测试样本)	运行总时间/s	单组数据运行时间/s	内存占用率峰值/%
BP神经网络	87.88	86.5	21.553 7	0.010 4	1.62
支持向量机	89.50	89.1	15.482 9	0.004 1	0.82
DBN	93.01	92.7	23.105 9	0.011 5	1.99

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