Rejection Algorithm for Low-resolution Radar Target Classification According to Minimum Cost
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摘要: 为解决低分辨雷达目标自动识别中，干扰目标、虚假目标的存在以及不同类别目标样本集混叠的问题，提出了一种基于最小代价的拒判K近邻识别算法.该算法根据雷达识别系统最小代价的原则，利用Fisher判别函数，确定拒判门限.设计了基于两类拒判域的K近邻识别算法，第一类拒判根据训练样本集特征值的波动范围，对干扰目标和虚假目标进行拒判；第二类拒判根据测试样本与最近邻、次近邻的距离差，实现混叠区域的目标样本拒判.算法先对测试样本进行拒判分析，再利用K近邻算法识别分类.实验结果表明，基于以上算法的低分辨雷达目标识别系统具有较好的鲁棒性和识别性能.Abstract: In order to solve the problem of interference target, false target and aliasing of different types of targets in low-resolution radar automatic recognition systems, a new K-nearest neighbor algorithm based on minimum cost is proposed. According to the principle of minimum cost of radar recognition system, Fisher discrimination is used to determine the rejection threshold. Two kinds of rejection regions are designed. The first kind rejection region, according to the fluctuation range of training samples' characteristic values, is used to reject interference targets and false targets. The second kind is used for the targets in aliasing region. Firstly, the test sample is analyzed by rejection region, then the K-nearest neighbor algorithm is used for recognition and classification. Experiment results show that the low-resolution radar target recognition system based on the above algorithm has better robustness and recognition performance.
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Key words:
- Low-resolution radar /
- target classification /
- rejection region /
- K-nearest neighbor
1) 本文责任编委 桑农 -
图 12 平均识别率随$\mathit{K}$值的变化
Fig. 12 Average recognition rate with different $\mathit{K}$
图 14 不同比例的训练样本各种方法的平均识别率
Fig. 14 Average recognition rate using different percent of training samples per class
表 1 标准数据集
Table 1 Standard datasets
Datasets Wine Iris Yeast Ecoli Winequality white Winequality red Seeds Vertebral column Instances 178 150 1 484 336 4 898 1 599 210 310 Attributes 13 4 8 7 11 11 7 6 Classes 3 3 10 8 7 6 3 3 
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表 2 拒判样本数目
Table 2 The number of rejection sample
拒判门限 0.01 0.03 0.05 0.07 0.09 0.11 0.13 0.15 0.17 0.19 0.21 0.23 0.25 人一类拒判 0 0 0 0 0 0 1 2 3 4 7 9 11 人二类拒判 5 15 26 35 44 61 81 98 115 142 170 202 225 摩托一类拒判 0 0 1 1 1 1 6 10 13 14 15 23 36 摩托二类拒判 2 5 6 10 12 12 13 16 16 18 21 23 24 卡车一类拒判 0 0 0 0 1 1 2 4 8 22 48 84 139 卡车二类拒判 0 1 2 3 3 3 3 4 5 6 6 7 7 库外一类拒判 18 30 38 48 64 80 89 105 120 137 158 173 193 库外二类拒判 142 136 133 130 120 114 112 107 101 94 84 79 68
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表 3 对雷达目标数据集使用5 %的训练样本各个算法的分类正确率(%)
Table 3 Classification accuracy for the radar target dataset using 5 % training samples per class (%)
# 类别 训练样本 测试样本 KNN WKNN SVM WSVM GA-SVM PSO-SVM 本文方法 1 人 20 378 64.47 75.42 78.21 79.13 80.24 80.71 77.85 2 摩托车 16 303 50.22 70.45 70.08 69.40 71.45 72.09 72.10 3 卡车 20 373 74.64 93.04 90.84 91.89 93.41 92.63 98.06 平均识别率 63.11 79.63 79.71 80.14 81.70 81.81 82.67 识别率标准差 2.13 1.67 1.92 1.90 1.69 1.73 1.77
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