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摘要:
干扰效能评估作为多属性决策问题时，为解决传统的逼近理想解排序法（TOPSIS）在进行雷达对抗效能评估时过于客观而不能充分体现评估者意志，并且在使用过程中仅考虑指标间欧氏距离导致某些处于正负理想解中垂线上的点无法分辨的问题，提出一种G1-变异系数-KL改进TOPSIS雷达对抗干扰有效性评估算法。该算法利用G1法和变异系数法分别求得主观和客观权重，并引入差异系数的概念充分反映主客观程度，利用相对熵解决位于正负理想解中垂线上的点无法排序的问题。通过仿真实验可以发现，提出的算法在评价干扰有效性时性能优于传统算法。
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关键词:
- 雷达对抗干扰有效性评估 /
- 逼近理想解排序法(TOPSIS) /
- G1法 /
- 变异系数法 /
- 相对熵 /
- 差异系数
Abstract:When jamming effectiveness evaluation is transformed to a multi-attribute decision-making problem, traditional Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is too objective to fully reflect the will of the evaluator, and the method only considers the inter-index Euclidean distance during the usage, which causes that certain solutions on the vertical line of the positive and negative ideal solutions cannot be distinguished. This paper proposes a G1-variation-coefficient-KL based TOPSIS radar jamming effectiveness evaluation algorithm. This method uses the G1 method and the variation coefficient method to obtain the subjective and objective weight, and introduces the coefficient of difference which can fully reflect the subjective and objective degree. With the application of relative entropy, the problem that the solutions on the vertical line of the positive and negative ideal solution cannot be sorted is solved, simulation results show that the performance of the proposed algorithm is better than some traditional algorithms in evaluating the effectiveness of jamming.
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图 1 雷达对抗干扰有效性评估2层指标
Figure 1. Two levels indicator of radar anti-jamming effectiveness evaluation
图 2 G1-变异系数-KL改进TOPSIS算法流程
Figure 2. Flowchart of G1-variation-coefficient-KL TOPSIS algorithm
图 3 不同算法的雷达对抗有效性评估结果
Figure 3. Radar anti-jamming effectiveness evaluation results of different algorithms
表 1 雷达反馈数据
Table 1. Radar feedback data
评估对象 脉冲重复频率/kHz 带宽/MHz 脉冲压缩比 脉冲宽度/μs 波束偏移角度/(°) 峰值功率/kW 波束驻留时间/s 1 4.103 1.056 34.677 34.454 0.787 21.385 1.769 2 5.053 1.083 48.720 44.550 0.351 21.788 1.123 3 6.855 2.849 198.153 70.137 0.637 24.623 1.593 4 14.238 5.595 81.418 14.525 0.647 47.285 1.646 5 10.176 1.864 153.256 77.489 0.183 53.059 2.011 
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表 2 归一化评估矩阵
Table 2. Normalized evaluation matrix
评估对象 a1 a2 a3 a4 a5 a6 a7 1 0.478 0.622 0.263 0.631 0.128 0.157 0.288 2 0.304 0.278 0.268 0.571 0.181 0.161 0.372 3 0.431 0.504 0.303 0.459 0.734 0.424 0.586 4 0.445 0.511 0.583 0 0.301 0.832 0.121 5 0.544 0.145 0.654 0.253 0.567 0.277 0.648
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表 3 G1-变异系数-KL改进TOPSIS算法有效性评估贴近度
Table 3. Effectiveness evaluation closeness using G1-variation-coefficient-KL TOPSIS
评估对象 1 2 3 4 5 贴近度 0.419 0.513 0.585 0.476 0.386
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表 4 极大熵法权重有效性贴近度
Table 4. Effectiveness evaluation closeness using maximum entropy to calculate weight
评估对象 1 2 3 4 5 贴近度 0.410 0.574 0.582 0.440 0.347
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表 5 传统TOPSIS算法的有效性贴近度
Table 5. Effectiveness evaluation closeness traditional TOPSIS algorithm
评估对象 1 2 3 4 5 贴近度 0.410 0.499 0.596 0.464 0.415
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表 6 雷达对抗干扰有效性评估结果
Table 6. Anti-jamming effectiveness evaluation results
评估对象 G1-变异系数-KL改进TOPSIS算法 极大熵法赋权 传统TOPSIS算法 贴近度 排序结果 贴近度 排序结果 贴近度 排序结果 1 0.419 4 0.410 4 0.410 5 2 0.513 2 0.574 2 0.499 2 3 0.585 1 0.582 1 0.596 1 4 0.476 3 0.440 3 0.464 3 5 0.386 5 0.347 5 0.415 4
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