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Identification Method for Icosahedron Triangular Trihedral Corner Reflector and Vessels Based on Polarization Feature-range Joint Matrix
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摘要: 在雷达目标识别领域，二十面体角反射器的引入无疑提升了目标辨识任务的难度。这种情况在中高海况下将尤为严重。剧烈起伏的海面将与二十面体角反射器形成耦合散射，这可能达到与目标相似的散射特性，从而导致传统目标辨识方法性能下降。针对中高海况下目标辨识难的问题，该文从主要散射机理和散射复杂程度两个方面，构建了极化特征-距离联合矩阵，表征中高海况下舰船与二十面体角反射器阵列之间的差异。然后，利用时序神经网络提取两者极化特征-距离联合矩阵的特征，以实现对目标的有效辨识。经数据集的验证，所提出的方法可以有效减少手工知识提取过程中的信息丢失。在中高海况条件下，相较于现有方法，方法的准确率提升了10.14%，大幅降低了二十面体角反射器阵列造成的虚警。Abstract: In the field of radar target recognition, the introduction of Icosahedron Triangular Trihedral Corner Reflector (ITTCR) has increased the difficulty of target identification tasks, especially under moderate to high sea states. Under such conditions, the undulating sea surface can couple with an ITTCR to produce scattering characteristics similar to those of the target, resulting in a decline in the performance of traditional target identification methods. As a solution, a joint matrix of polarization features and range was constructed by considering the dominant scattering mechanisms and scattering complexity. This matrix characterizes the component-level differences between ships and ITTCR arrays in the presence of sea clutter. Subsequently, a temporal neural network extracts features from the joint matrices of the vessels and ITTCR arrays, achieving effective target identification. The performance of the proposed method was validated using a dataset. The proposed method effectively reduces information loss during manual knowledge refinement. Under moderate to high sea states, the proposed method has an accuracy higher than that of the existing methods by 10.14%. Furthermore, the proposed method considerably reduces false alarms caused by ITTCR arrays.
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图 1 各表征方式的二面角与其他典型结构幅度相似性
Figure 1. The amplitude similarity of between dihedral and other typical structures by various representation method
图 7 低掠射角下角反阵列的极化散射特性
Figure 7. Polarization scattering characteristics of ITTCRA at low grazing angles
图 8 低掠射角中高海况不同接收极化状态的舰船与角反阵列HRRP
Figure 8. Typical vessel and ITTCRA HRRP under moderate to high sea state for various receive polarization states at low grazing angle
图 9 高掠射角下角反阵列的极化散射特性
Figure 9. Polarization scattering characteristics of ITTCRA at high grazing angles
图 10 高掠射角中高海况不同接收极化状态的舰船与角反阵列HRRP
Figure 10. Typical vessel and ITTCRA HRRP under moderate to high sea state for various receive polarization states at high grazing angle
表 1 典型散射体极化散射矩阵
Table 1. Typical scatterer polarization scattering matrix
几何结构 PSM 二面角 $ \left[ {\begin{array}{*{20}{c}} {\text{1}}&{\text{0}} \\ {\text{0}}&{{{ - 1}}} \end{array}} \right] $ 三面角 $ \left[ {\begin{array}{*{20}{c}} {\text{1}}&{\text{0}} \\ {\text{0}}&{\text{1}} \end{array}} \right] $ 圆柱体 $ \left[ {\begin{array}{*{20}{c}} {\text{1}}&{\text{0}} \\ {\text{0}}&{\dfrac{{\text{1}}}{{\text{2}}}} \end{array}} \right] $ 螺旋体 $ \dfrac{1}{2}\left[ {\begin{array}{*{20}{c}} {\text{1}}&{\text{j}} \\ {\text{j}}&{ - 1} \end{array}} \right] $ 窄二面角 $ \left[ {\begin{array}{*{20}{c}} {\text{1}}&0 \\ 0&{ - \dfrac{1}{2}} \end{array}} \right] $ 
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表 2 海杂波背景角反阵列电磁仿真参数
Table 2. ITTCRA electromagnetic simulation parameters in sea clutter background
参数名称 详细指标 仿真目标 角反阵列、典型舰船 载频 16 GHz 带宽 300 MHz 方位角 0°～180°(线阵)，0°～180°(面阵)，间隔 5° 俯仰角 15°, 90° 频点个数 601
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表 3 实验雷达参数
Table 3. Experimental radar parameters
编号 参数名称 详细指标 1 发射方式 水平极化、垂直极化分时发射 2 接收方式 水平极化和、垂直极化和
与垂直极化差同时接收3 工作中心频率 7 GHz 4 脉冲重复周期 50 μs 5 分时发射脉冲延时 25 μs 6 发射信号调制方式 线性调频 7 发射信号脉宽 20 μs 8 发射信号带宽 150 MHz 9 基带回波采样率 300 MHz
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表 5 采样点数N性能分析
Table 5. Performance analysis of sampling points N
N 准确度(%) 精确度(%) 召回率(%) F1 Score(%) 2 87.92 83.53 86.93 84.95 3 89.37 85.43 87.91 86.54 4 91.30 88.59 88.59 88.59 5 91.79 88.30 91.39 89.65 6 91.79 88.75 90.15 89.41
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表 6 同海况各辨识网络性能对比
Table 6. Performance comparison of identification networks under the same sea states
辨识网络 准确度(%) 精确度(%) 召回率(%) F1 Score (%) ResNet18 90.34 87.04 87.94 87.47 BiGRU 90.82 87.80 88.26 88.03 所提方法 91.30 88.59 88.59 88.59
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表 7 不同海况下各辨识网络性能对比
Table 7. Performance comparison of identification networks under different sea states
辨识网络 准确度(%) 精确度(%) 召回率(%) F1 Score (%) ResNet18 86.94 85.98 84.12 84.93 BiGRU 88.74 88.12 86.15 87.01 所提方法 89.19 88.25 87.16 87.67
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表 8 网络输入性能对比分析
Table 8. Performance comparison analysis of network input
网络输入方式 准确度(%) 精确度(%) 召回率(%) F1 Score (%) PSM 79.71 73.34 72.75 73.03 Pauli分解 80.68 74.94 71.54 72.88 Krogager分解 84.54 80.02 78.47 79.19 所提方法 91.30 88.59 88.59 88.59
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