UUV threat assessment method based on EBM
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摘要: 针对传统的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, 文中提出了一种创新的解决方案: 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, 巧妙地融合了梯度提升与广义加性模型(GAM), 实现了线性模型的高可解释性与梯度提升算法的准确性的完美结合。文中对EBM模型的性能进行了全面评估, 并与其他几种主流机器学习方法进行了比较, 包括分类提升、自适应提升以及深度学习。通过仿真实验, 发现EBM模型在保持高可解释性的同时, 还展现出了卓越的准确率, 在威胁等级的识别上, EBM模型的准确度达到了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 threat assessment methods process complex battlefield situation data, this paper proposes an innovative solution: unmanned undersea vehicle(UUV) threat assessment model 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 study, the performance of the EBM model was comprehensively evaluated and compared with several other mainstream machine learning methods, including CatBoost, AdaBoost, and Deep Learning. Through simulation experiments, we found that the EBM model not only maintained high interpretability, but also showed excellent accuracy, the accuracy of the EBM model reached 98.10% in the identification of threat levels. This result not only verifies the effectiveness of the EBM model in complex battlefield situation analysis, but also provides a solid theoretical foundation and technical support for UUV's autonomous decision-making.
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图 2 机器学习算法的可解释性与准确率
Figure 2. Explainability and accuracy of machine learning algorithms
图 10 目标类型对结果影响和目标类型分布
Figure 10. target types affects the results and target types distribution
图 11 毁伤能力对结果影响和毁伤能力分布
Figure 11. damage capabilities affects the results and damage capabilities distribution
图 12 探测能力可视化和探测能力分布
Figure 12. 12 detection capabilities affects the results and detection capabilities distribution
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 REMUS 600
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 enemy 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 enemy 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. partially includes 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. EBM model parameter settings
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 $soft\max $
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