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Recognition of Ships and Chaff Clouds Based on Sophisticated Polarimetric Target Decomposition
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摘要: 用于干扰舰船目标的箔条云通常具有与舰船目标相近的尺寸和雷达散射截面积，这使得舰船与箔条云的识别成为一个非常有挑战性的问题。该文提出一种基于精细极化目标分解的识别方法。为了能够有效地识别舰船目标与箔条云，该文首先结合3种精细化散射模型，提出了一种基于精细散射模型的七成分分解方法。通过这种分解方法可以有效地刻画舰船目标的散射特性。为了将舰船与箔条云的极化特性进行有效的对比和区分，该文根据分解得到的散射成分贡献构造了一个稳健的散射贡献差特征。最后，通过将构造的散射贡献差与极化散射角结合，构造了新的特征矢量并利用支持向量机实现了最终的识别。实验利用仿真和实测的极化雷达数据对所提方法进行了验证，结果表明该方法优于现有的其他方法，并能够达到最高98%的正确识别率。Abstract: The recognition of ships from chaff cloud jamming is challenging because they have similar dimensions and radar cross sections. In this paper, we propose a polarimetric recognition technique with sophisticated polarimetric target decomposition. Three sophisticated scattering models are integrated to constitute a seven-component model-based decomposition method so as to accurately characterize the dominant and local scattering of ships. Based on the concepts of contrast and suppression, a robust scattering contribution difference feature is designed according to the derived scattering contributions. The constructed feature vector, combined with the polarization scattering angle, is inputted into the support vector machine to fulfill the recognition process. Simulated and real polarimetric radar data are utilized to test the proposed method, and the results show that the proposed method outperforms state-of-the-art methods by achieving the highest recognition rate of over 98%.
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图 1 真实场景中从舰船上释放的箔条云(图片来源：百度)
Figure 1. Chaff releasing from a ship in an actual scenario (Courtesy: Baidu)
图 3 基于支持向量机的舰船识别流程图
Figure 3. The flowchart of ship recognition based on the support vector machine
图 4 箔条云仿真数据和舰船目标实测极化雷达数据
Figure 4. Simulated and real polarimetric radar data of chaff clouds and ships
图 5 箔条云在不同情况下的分解结果
Figure 5. Decomposition results for chaff clouds in different cases
图 7 箔条云极化散射角直方图
Figure 7. Histograms of polarization scattering angle for chaff clouds
图 10 不同组合方法的识别结果
Figure 10. SVM-based classification results of different composite methods
表 1 全极化SAR仿真系数
Table 1. Fully polarized SAR simulation parameter
参数 取值 参数 取值 平台速度 400 m/s 方位向波束宽度 0.5° 信号载频 35 GHz 脉冲重复频率 400 Hz 信号带宽 150 MHz 平台高度 20 km 信号脉宽 5 μs 斜视角 70° 最近斜距 20 km 
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表 2 箔条云散射成分统计结果(%)
Table 2. Scattering contribution statistics for chaff clouds(%)
散射类型 情况1 情况2 情况3-1 情况3-2 表面散射 2.71 22.29 38.95 16.91 二次散射 0.86 0.04 0.14 0.08 体散射 93.52 77.13 60.50 82.45 螺旋体散射 0.53 0.40 0.36 0.42 OOD散射 0.55 0.12 0.05 0.06 ±45°OD 散射 0.92 0 0 0.02 ±45°OQW 散射 0.90 0.01 0 0.03 复杂结构散射 2.90 0.53 0.41 0.53
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表 3 舰船目标散射成分统计结果(%)
Table 3. Scattering contribution statistics for ships (%)
散射类型 T1 T2 T3 T4 T5 T6 T7 表面散射 5.030 13.200 31.030 3.840 30.220 42.280 40.830 二次散射 83.560 84.710 55.050 95.300 32.460 12.000 12.080 体散射 5.350 1.530 7.890 0.610 23.830 30.170 33.070 螺旋体散射 2.690 0.120 2.410 0.060 7.410 6.460 9.990 OOD散射 0.300 0.050 0.110 0.030 1.480 0.490 0.060 ±45°OD散射 1.490 0.030 1.040 0.004 2.670 4.370 2.020 ±45°OQW散射 1.190 0.030 2.030 0.008 2.800 4.110 1.710 复杂结构散射 5.670 0.230 5.590 0.100 14.360 15.430 13.780
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表 4 不同组合方法定量识别性能(%)
Table 4. Quantitative recognition performance for different composite methods(%)
识别方法 正确识别率 漏检率 错误识别率 分类精度 本文方法 98.69 1.31 0.25 100.00 极化比-极化散射角-支持向量机 91.95 8.05 0 90.90 泛化体散射+极化散射角+支持向量机 94.29 5.71 0.89 100.00 散射贡献差-支持向量机 94.94 5.06 3.63 100.00 极化散射角-支持向量机 94.39 5.61 3.91 90.90
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