Target Threat Assessment Method for UUVs Based on EBM
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摘要: 针对传统的目标威胁评估方法处理复杂战场态势数据时, 缺乏数据挖掘能力和神经网络算法解释性不足等问题, 提出基于可解释增强机(EBM)的无人水下航行器(UUV)对目标威胁评估模型。EBM作为一种先进的机器学习技术, 巧妙融合了梯度提升与广义加性模型, 实现了线性模型的高可解释性与梯度提升算法准确性的完美结合。文中对EBM模型的性能进行了全面评估, 并与分类提升、自适应提升以及深度学习等几种主流机器学习方法进行了比较。通过仿真实验发现, EBM模型在保持高可解释性的同时, 对威胁等级识别的准确度也高达98.10%。这一结果不仅验证了EBM模型在复杂战场态势分析中的有效性, 也为UUV的自主决策提供了坚实的理论基础和技术支持。Abstract: In order to solve the problems of lack of data mining ability and insufficient explanatory nature of neural network algorithms when traditional target threat assessment methods process complex battlefield situation data, this paper proposed a threat assessment model for unmanned undersea vehicles(UUVs) based on explainable boosting machine(EBM). As an advanced machine learning technology, EBM cleverly integrates gradient boosting and generalized additive model to achieve the perfect combination of high interpretability of linear model and accuracy of gradient boosting algorithm. In this paper, the performance of the EBM model was comprehensively evaluated and compared with several other mainstream machine learning methods, including classification enhancement, adaptive enhancement, and deep learning. The simulation experiments find that the EBM model not only maintains high interpretability but also has an accuracy of 98.10% in the identification of threat levels. This result verifies the effectiveness of the EBM model in complex battlefield situation analysis and provides a solid theoretical foundation and technical support for UUV’s autonomous decision-making.
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图 1 敌我双方相对角度示意图
Figure 1. Schematic diagram of the relative angle between the enemy and our side
图 2 不同机器学习算法的可解释性与准确率
Figure 2. Explainability and accuracy of different machine learning algorithms
表 1 目标类型特点及威胁度量化表
Table 1. Characteristics of target types and threat quantification
目标类型 特点 威胁
隶属度巡逻艇 探测能力强、携带武器多、隐蔽性弱 0.5 鱼雷 航速大、体积小、杀伤力强、隐蔽性较强 0.9 USV 航速较大、隐蔽性较弱 0.4 中型UUV 航速小、隐蔽性强 0.2 
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表 2 目标毁伤能力威胁度量化表
Table 2. Quantification of the threat of target damage capability
目标类型 巡逻艇 USV UUV 鱼雷 毁伤能力 0.8 0.6 0.4 0.2
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表 3 目标探测能力威胁度量化表
Table 3. Quantification of the threat of target detection capabilities
目标类型 巡逻艇 USV UUV 鱼雷 探测能力 0.8 0.4 0.6 0.2
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表 4 部分样本数据
Table 4. Partial sample data
目标
类型毁伤
能力探测
能力距离
量化速度
量化角度
量化威胁
等级USV 0.6 0.6 0.1 0.41 0.42 1 巡逻艇 0.8 0.8 0.83 0.95 0.32 3 UUV 0.4 0.4 0.1 0.1 0.24 0 鱼雷 0.2 0.2 0.56 0.17 0.93 4
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表 5 EBM模型参数设置
Table 5. Parameter settings of the EBM model
参数 值 参数 值 输入特征个数 6 学习率 0.35 输出节点个数 5 最大分箱数 35 最大叶子数 2 外袋数量 1 最小海森值 0.5 链接函数 $ \mathrm{Soft}\max $
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