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摘要:
针对参数区间为交叉类型的目标识别问题,提出了基于直觉模糊集和云模型的逼近理想点(TOPSIS)识别方法。构建了包含个体类和交叉类的目标数据库模型,根据云模型的多步估计算法,得到未知目标相对已知目标类的确定度,提出了确定度向隶属度和非隶属度的转化方法,基于直觉模糊熵计算特征属性的动态权重,形成了去模糊距离测度的TOPSIS识别方法,应用于辐射源信号识别。仿真结果表明,所提方法对参数区间交叉类型的目标正确识别率较高,具有一定的实际应用价值。
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关键词:
- 区间交叉 /
- 直觉模糊集 /
- 云模型 /
- 动态权重 /
- 逼近理想点(TOPSIS)
Abstract:Aimed at the problem of target recognition with parameters of interval cross type, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method for target recognition based on intuitionistic fuzzy set and cloud model is proposed in this paper. The target database model including individual class and cross class is constructed. According to the multi-step estimation algorithm for cloud model, the certainty degree of an unknown target over a known target class is obtained, and the transformation algorithm from certainty degree to membership and non-membership degree is proposed. The dynamic attribute weight is calculated based on intuitionistic fuzzy entropy. The TOPSIS recognition decision method of defuzzification distance measure is formed. The simulation results indicate that the proposed method has a high accuracy rate for target recognition with parameters of interval cross type and thus has a certain practical application value, when applied to radar emitter recognition.
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表 1 雷达数据库
Table 1. Radar database
序号 个体雷达类 射频频率
RF/MHz脉冲重复周期
RPI/μs脉宽
PW/μs1 R1 [4 940, 5 160] [3 680, 3 750] [0.6, 1.2] 2 R2 [5 000, 5 220] [3 630, 3 700] [0.2, 0.5] 3 R3 [5 100, 5 420] [3 580, 3 650] [0.4, 0.7] 4 R4 [5 400, 5 520] [3 730, 3 800] [0.6, 0.9] 5 R5 [5 480, 5 620] [3 490, 3 600] [1, 1.4] 6 R12 [5 000, 5 160] [3 680, 3 700] 7 R13 [5 100, 5 160] [0.6, 0.7] 8 R14 [3 730, 3 750] [0.6, 0.9] 9 R15 [1, 1.2] 10 R23 [5 100, 5 220] [3 630, 3 650] [0.4, 0.5] 11 R34 [5 400, 5 420] [0.5, 0.7] 12 R35 [3 580, 3 600] 13 R45 [5 480, 5 520] 14 R123 [5 100, 5 160] 15 R134 [0.6, 0.7] 表 2 不同云模型的仿真结果
Table 2. Simulation results of different cloud models
表 3 不同权重计算方法的仿真结果
Table 3. Simulation results of different weight calculation methods
权重计算方法 本文 文献[10] 等权重方法 正确识别率/% 94.2 65.4 93.5 表 4 仿真环境1的正确识别率
Table 4. Correct recognition rate of simulation environment 1
获取测量值的方式 正确识别率/% 本文方法 文献[9]方法 R1类区间内随机抽取 93.6 86.4 R2类区间内随机抽取 89.4 90.3 R3类区间内随机抽取 94.2 91.5 R4类区间内随机抽取 94.1 91.8 R5类区间内随机抽取 99.9 97.9 数据库区间内随机抽取 94.6 92.6 表 5 仿真环境2的正确识别率
Table 5. Correct recognition rate of simulation environment 2
获取测量值的方式 本文方法正确识别率/% 文献[9]方法正确识别率/% 情况1 情况2 情况3 情况1 情况2 情况3 R1类区间外随机抽取 82.3 46.6 78 69.3 42.7 44.3 R2类区间外随机抽取 69.9 54 75 78.8 62.6 55.3 R3类区间外随机抽取 69 68.7 80.7 73.3 64.9 49.4 R4类区间外随机抽取 74.3 73 87 63 55.6 50.7 R5类区间外随机抽取 97.1 98 99.7 90.5 87.3 75.3 数据库区间外随机抽取 78.8 68.5 85.3 75.8 61.5 57 -
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