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SAR Target Physics Interpretable Recognition Method Based on Three Dimensional Parametric Electromagnetic Part Model
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摘要: 该文通过部件级三维参数化电磁模型(3D-PEPM)描述了复杂目标的电磁散射现象，并基于此模型提出了一种新的合成孔径雷达(SAR)目标识别方法。该方法首先根据雷达参数将3D-PEPM中各个散射体的散射响应投影到二维图像平面，预测每个散射体的位置和形状，然后根据3D-PEPM提供的先验信息评估3D-PEPM与SAR数据之间的相似程度，最后利用一种视角调整方法对整个过程进行优化，产生3D-PEPM和SAR数据之间的最终匹配分数，并根据该匹配分数完成识别决策。这种识别方法明确标识了SAR数据和3D-PEPM散射体之间的对应关系，具有清晰的物理可解释性，能够有效处理各种扩展条件下的SAR目标识别问题，仿真实验验证了该方法的有效性。Abstract: In this paper, a target’s electromagnetic scattering phenomenon is characterized by the Three Dimensional Parametric Electromagnetic Part Model (3D-PEPM) and a novel Synthetic Aperture Radar (SAR) target recognition method is proposed based on the model. The proposed method projects the individual scatterers in the 3D-PEPM to the 2D image plane to predict the location and appearance for each scatterer according to the radar parameters firstly. Then based on the prior information provided by the 3D-PEPM, the similarities between the 3D-PEPM and SAR data are evaluated. Finally, a view angle adjusting method is utilized to optimize the whole process to produce the final match score between the model and SAR data, and the recognition decision is made according to the match score. The proposed recognition method identifies clearly the correspondences of the scatterers between SAR data and 3D-PEPM and enjoys the explicit physical interpretability, so it can deal with SAR recognition problems under various extended operating conditions. Experiments on simulated data reveal the effectiveness of the proposed method.
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图 7 模型和EM仿真软件产生的目标RCS对比
Figure 7. Comparison of target RCS generated by 3D-PEPM and EM simulation software
图 8 不同视角下基于3D-PEPM生成的图像
Figure 8. Images generated from different perspectives based on 3D-PEPM
图 9 相同视角下3个模型的观测图像
Figure 9. Observation images of three models in the same perspective
图 10 模型图像和3D-PEPM物理相关的散射体
Figure 10. Model image and 3D-PEPM physically related scatterers
图 11 模型图像和3D-PEPM物理相关的散射体
Figure 11. Model image and 3D-PEPM physically related scatterers
图 12 相同视角下3个模型的观测图像
Figure 12. Observation images of three models in the same perspective
图 13 Slicy目标在不同信噪比下的仿真图像
Figure 13. The simulated images of the slicy target under different SNR
图 15 散射体遮挡下的相似度测量性能
Figure 15. Performance of similarity measurement under scatterer occlusion
表 1 参数的统计模型
Table 1. Statistical model of parameters
特征属性 均值 方差 距离向位置 $x$ ${x_0}$ ${\sigma^2 _x} = {f^2_{ {\rm{Downrange} } } }$ 方位向位置 $y$ ${y_0}$ ${\sigma^2 _y} = {f^2_{ {\rm{Crossrange} } } }$ 幅度 $\log 10(\left| A \right|)$ $\lg ({\left| A \right|_0})$ ${\sigma^2 _A} = 0.5$ 长度 $L$ ${L_0}$ ${\sigma^2 _L} = {(2{f_{ {\rm{Crossrange} } } })^2}$ 
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表 2 模型投影和从数据估计得到的散射体参数的对比
Table 2. Comparison between model projection and scatterer parameters estimated from data
序号 模型投影得到的散射体参数 从数据估计得到的散射体参数 相似度 X(m) Y(m) L(m) A X(m) Y(m) L(m) A 1 –1.932 0.567 4.416 1.000 –1.932 0.487 4.265 1.000 0.643 2 –0.901 1.772 0.649 0.532 –0.900 1.780 0 0.193 0.254 3 0.181 1.212 0.708 0.458 0.182 1.210 0.695 0.095 0.902 4 0.181 2.400 1.057 0.328 0.183 2.394 0.868 0.114 0.705 5 0.181 –1.126 0.668 0.091 0.178 –1.168 0.633 0.048 0.841 6 –1.601 0 0 0.067 –1.730 –0.001 0 0.076 0.664 7 –1.068 –3.031 4.377 0.052 –0.929 –2.879 4.276 0.044 0.351 8 –0.772 –1.264 0.647 0.010 –0.770 –1.262 0.697 0.018 0.922 9 –1.431 0 0 0.008 –1.548 –0.002 0 0.045 0.689 10 0.338 –2.598 0.325 0.006 0.340 –2.324 0 0.001 0.227
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