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摘要:
空中目标威胁评估是地面防空系统武器配置和资源管理的基础。针对威胁评估的实时性和人为主观性等问题，在综合粗糙集（RS）理论和逼近理想解排序法（TOPSIS）的基础上，建立了RS-TOPSIS空中目标威胁评估模型。通过RS理论对目标属性赋权，减少人为主观因素的影响与对先验信息的需求，进而结合TOPSIS分析贴近度并计算得到目标威胁程度。模型基于数据驱动，易于实现并具备良好的实时性。仿真结果表明该方法能有效评估目标威胁程度，为空中目标威胁程度的实时评估提供了一种新的工程决策方法。
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关键词:
- 粗糙集(RS) /
- 逼近理想解排序法(TOPSIS) /
- 威胁评估 /
- 地面防空 /
- 空中目标
Abstract:Air target threat evaluation is the foundation for weapon allocation and resource management within the ground surface air defense system. Aimed at the problems of real-time and human subjectivity for threat evaluation, an air target threat evaluation model based on RS-TOPSIS is established according to combining rough set (RS) theory and technique for order preference by similarity to ideal solution (TOPSIS). RS theory which can avoid the influence of subjective factors and the requirement for prior information is used to determine the weight of target attribute, then close degree is analyzed with TOPSIS, and threat degree of air target is obtained. The model driven with data is easy to implement and has good real-time performance. The simulation results show that this method can effectively evaluate the threat degree and thus provides a new engineering decision-making method for real-time evaluation of air target threat degree.
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图 2 RS-TOPSIS与TOPSIS相对贴近度对比
Figure 2. Comparison of relative similarity scale between RS-TOPSIS and TOPSIS
目标 目标类型 目标速度/(m·s-1) 目标航向角/(°) 目标干扰能力 目标高度 目标距离/m 目标威胁值 t1 大型目标 500 130 强 高 360 0.521 2 t2 大型目标 550 90 中 中 160 0.582 8 t3 大型目标 650 110 强 低 280 0.646 5 t4 小型目标 600 50 中 高 160 0.685 3 t5 小型目标 750 150 中 超低 400 0.754 1 t6 小型目标 640 180 强 中 280 0.676 4 t7 直升机 80 6 弱 高 180 0.347 1 t8 直升机 88 140 无 超低 320 0.359 2 t9 直升机 90 180 弱 低 170 0.347 4 
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表 2 离散化数据
Table 2. Data after discretization
目标 a1 a2 a3 a4 a5 a6 D t1 2 3 3 4 4 4 2 t2 2 3 2 3 3 2 3 t3 2 4 3 4 2 3 3 t4 3 3 1 3 4 2 4 t5 3 4 3 3 1 4 4 t6 3 4 4 4 3 3 3 t7 1 1 1 2 4 2 1 t8 1 1 3 1 1 4 1 t9 1 1 4 2 2 2 1
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表 3 决策辨识矩阵
Table 3. Decision discernibility matrix
目标 t1 t2 t3 t4 t5 t6 t7 t8 t9 t1 ∅ a3a4a5a6 a2a5a6 a1a3a4a6 a1a2a4a5 a1a2a3a5a6 a1a2a3a4a6 a1a2a4a5 a1a2a3a4a5a6 t2 a3a4a5a6 ∅ ∅ a1a3a5 a1a2a3a5a6 ∅ a1a2a3a4a5 a1a2a3a4a5a6 a1a2a3a4a5 t3 a2a5a6 ∅ ∅ a1a2a3a4a5a6 a1a4a5a6 ∅ a1a2a3a4a5a6 a1a2a4a5a6 a1a2a3a4a6 t4 a1a3a4a6 a1a3a5 a1a2a3a4a5a6 ∅ ∅ a2a3a4a5a6 a1a2a4 a1a2a3a4a5a6 a1a2a3a4a5 t5 a1a2a4a5 a1a2a3a5a6 a1a4a5a6 ∅ ∅ a3a4a5a6 a1a2a3a4a5a6 a1a2a4 a1a2a3a4a5a6 t6 a1a2a3a5a6 ∅ ∅ a2a3a4a5a6 a3a4a5a6 ∅ a1a2a3a4a5a6 a1a2a3a4a5a6 a1a2a4a5a6 t7 a1a2a3a4a6 a1a2a3a4a5 a1a2a3a4a5a6 a1a2a4 a1a2a3a4a5a6 a1a2a3a4a5a6 ∅ ∅ ∅ t8 a1a2a4a5 a1a2a3a4a5a6 a1a2a4a5a6 a1a2a4a5a6 a1a2a4 a1a2a3a4a5a6 ∅ ∅ ∅ t9 a1a2a3a4a5a6 a1a2a3a4a5 a1a2a3a4a6 a1a2a3a4a5 a1a2a3a4a5a6 a1a2a4a5a6 ∅ ∅ ∅
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表 4 各属性的加权标准化决策矩阵
Table 4. Weighted standard decision matrix of each attribute
目标 a1 a2 a3 a4 a5 a6 t1 0.166 7 0 0 0 0 0.055 6 t2 0.166 7 0 0 0 0.111 1 0.333 3 t3 0.166 7 0 0 0 0.222 2 0.166 7 t4 0.333 3 0 0 0 0 0.333 3 t5 0.333 3 0 0 0 0.333 3 0 t6 0.333 3 0 0 0 0.111 1 0.166 7 t7 0 0 0 0 0 0.305 6 t8 0 0 0 0 0.333 3 0.111 1 t9 0 0 0 0 0.222 2 0.319 4
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表 5 模型预测结果
Table 5. Prediction results of model
目标 正理想解距离 负理想解距离 相对贴近度 可能分类 原始分类 t1 0.464 7 0.175 7 0.277 4 1 2 t2 0.277 7 0.388 9 0.583 4 3 3 t3 0.260 5 0.324 0 0.554 3 3 3 t4 0.333 3 0.471 4 0.585 8 4 4 t5 0.333 3 0.471 4 0.585 8 4 4 t6 0.277 7 0.388 9 0.583 4 3 3 t7 0.472 2 0.305 6 0.392 9 1 1 t8 0.400 6 0.351 3 0.467 2 1 1 t9 0.351 6 0.389 1 0.525 3 1 1
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