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Refocusing Satellite Antenna Motion in Compact Polarimetric ISAR Images Using a Spatial-Frequency Feature-Guided Brownian Bridge Diffusion Model
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摘要: 极化逆合成孔径雷达(ISAR)是获取卫星目标高分辨率信息的重要手段。简缩极化能够平衡系统复杂度与极化信息容量，目前已广泛应用于各类地基ISAR系统。然而，在卫星过境时，为了调整观测区域，其搭载的天线部件通常会存在相对于卫星本体的独立机械转动。这类运动部件会在回波中引入额外的频率调制，造成ISAR图像散焦。基于深度学习的ISAR图像重聚焦方法通常侧重于目标整体的重聚焦，而忽略了运动部件所引起的频率特征变化，从而导致模型的聚焦性能有限。针对上述问题，该文提出了一种基于空频特征引导布朗桥扩散模型的简缩极化ISAR卫星天线运动重聚焦方法。该方法的核心思想是基于布朗桥扩散模型学习散焦ISAR图像和聚焦ISAR图像之间的映射关系，同时提取ISAR图像的空间特征和频率特征用于引导模型的学习，进而实现运动部件重聚焦。在此基础上，构建了极化ISAR卫星目标电磁仿真数据集并开展了对比实验，结果验证了所提方法具有更好的运动部件聚焦性能和泛化性能。Abstract: Polarimetric inverse synthetic aperture radar (ISAR) is an important tool for obtaining high-resolution information about satellite targets. Compact polarization provides an optimal balance between system complexity and polarization information capacity and has been widely used in various ground-based ISAR systems. However, during satellite transit, the onboard antenna components often undergo independent mechanical rotation relative to the satellite main body to adjust the observation area. This motion introduces additional frequency modulation into the echo signals, resulting in defocused ISAR images. Existing deep learning-based refocusing methods primarily focus on the global refocusing of ISAR images and often overlook the frequency feature variations induced by component motion, thereby limiting the model’s focusing capabilities. To address these challenges, this study proposes a novel refocusing method for satellite antenna motion in compact polarimetric ISAR imaging based on a spatial-frequency feature-guided Brownian bridge diffusion model. The core idea behind this method is to utilize the Brownian bridge diffusion model to learn the intricate mapping between defocused and focused ISAR images. Simultaneously, spatial and frequency features are extracted from the ISAR images to guide this learning process, thereby achieving precise refocusing of the moving components. To validate this approach, an electromagnetic simulation dataset for polarimetric ISAR satellite targets is established, and comparative experiments are conducted. The results show that the proposed method achieves superior performance in refocusing moving components and exhibits enhanced generalization capabilities.
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图 1 卫星天线运动重聚焦布朗桥扩散模型结构图
Figure 1. Schematic diagram of the Brownian bridge diffusion model for refocusing moving components in satellite targets
图 2 简缩极化ISAR卫星天线运动重聚焦方法框架
Figure 2. Compact polarization ISAR satellite target moving components refocusing method framework
图 3 含典型天线运动的卫星电磁仿真示意图
Figure 3. Schematic diagram of satellite electromagnetic simulation with typical moving components
图 4 4类卫星不同方法$ {S}_{\mathrm{LL}} $通道图像聚焦对比结果
Figure 4. Comparison results of $ {S}_{\mathrm{LL}} $image focusing using different methods for four types of satellites
图 5 4类卫星不同方法$ {S}_{\mathrm{RL}} $通道图像聚焦对比结果
Figure 5. Comparison results of $ {S}_{\mathrm{RL}} $image focusing using different methods for four types of satellites
图 6 4类卫星不同方法$ {S}_{\mathrm{LL}} $通道图像部件不同转速聚焦对比结果
Figure 6. Comparison results of $ {S}_{\mathrm{LL}} $image focusing using different methods for four satellites under different rotational speeds of components
图 7 4类卫星不同模型$ {S}_{\mathrm{LL}} $通道图像聚焦对比结果
Figure 7. Comparison results of $ {S}_{\mathrm{LL}} $image focusing using different models for four types of satellites
图 8 4类卫星不同模型$ {S}_{\text{R}\mathrm{L}} $通道图像聚焦对比结果
Figure 8. Comparison results of $ {S}_{\text{R}\mathrm{L}} $ image focusing using different models for four types of satellites
表 1 卫星天线运动重聚焦不同场景数据情况
Table 1. Data scenarios for refocusing of moving components in satellite target
场景 俯仰角 数据数量 $ -\!\!\!\!\lambda_{\text{train}}^{\mathrm{all}} $ 40°、45°、50° 876组 $ -\!\!\!\!\lambda_{\text{train}}^{40,45} $ 40°、45° 584组 $ -\!\!\!\!\lambda_{\text{train}}^{45,50} $ 45°、50° 584组 $ -\!\!\!\!\lambda_{\text{train}}^{40,50} $ 40°、50° 584组 $ -\!\!\!\!\lambda_{\text{test}}^{40} $ 40° 36组 $ -\!\!\!\!\lambda_{\text{test}}^{45} $ 45° 36组 $ -\!\!\!\!\lambda_{\text{test}}^{50} $ 50° 36组 
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表 2 不同重聚焦方法MAE指标结果
Table 2. MAE results of different refocusing methods
重聚焦
方法$ -\!\!\!\!\lambda_{\text{train}}^{40,45} $ $ -\!\!\!\!\lambda_{\text{train}}^{45，50} $ $ -\!\!\!\!\lambda_{\text{train}}^{40,50} $ $ -\!\!\!\!\lambda_{\text{train}}^{\mathrm{all}} $ MAE 均值↓ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ MiniEnt - - - - - - - - - 1.357 1.429 1.635 1.474 CV-GMTINet 0.948 1.199 1.445 0.936 1.146 1.314 0.905 1.173 1.321 0.940 1.189 1.381 1.158 CV-CFUNet 0.769 0.983 1.260 0.788 1.071 1.157 0.793 1.085 1.173 0.740 0.949 1.134 0.992 CVPHD 0.527 0.648 1.220 1.121 0.627 0.729 0.538 1.098 0.767 0.570 0.704 0.815 0.780 DDPM 0.824 0.834 0.817 0.840 0.810 0.766 0.841 0.819 0.810 0.759 0.766 0.746 0.803 所提方法 0.543 0.677 1.070 0.778 0.702 0.822 0.571 0.915 0.859 0.483 0.663 0.825 0.742 注：MiniEnt为传统的最小熵重聚焦方法，无需进行训练。
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表 5 不同重聚焦方法图像熵指标结果(bit)
Table 5. Image entropy results of different refocusing methods (bit)
重聚焦
方法$ -\!\!\!\!\lambda_{\text{train}}^{40,45} $ $ -\!\!\!\!\lambda_{\text{train}}^{45，50} $ $ -\!\!\!\!\lambda_{\text{train}}^{40,50} $ $ -\!\!\!\!\lambda_{\text{train}}^{\mathrm{all}} $ 熵
均值↓$ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ MiniEnt - - - - - - - - - 7.589 7.614 7.965 7.723 CV-GMTINet 6.812 6.905 7.523 7.418 6.709 6.814 6.721 7.307 6.613 6.508 6.411 6.506 6.854 CV-CFUNet 6.307 6.413 7.019 6.904 6.208 6.312 6.215 6.809 6.106 6.003 5.907 6.011 6.351 CVPHD 5.809 5.914 6.522 6.417 5.706 5.811 5.713 6.308 5.605 5.502 5.409 5.510 5.852 DDPM 6.125 6.224 6.103 6.358 6.225 6.079 6.154 6.056 5.911 5.267 5.342 6.124 5.997 所提方法 4.803 4.908 5.412 5.306 4.704 4.809 4.711 5.207 4.605 4.402 4.308 4.406 4.798 注：MiniEnt为传统的最小熵重聚焦方法，无需进行训练。
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表 3 不同重聚焦方法SSIM指标结果
Table 3. SSIM results of different refocusing methods
重聚焦
方法$ -\!\!\!\!\lambda_{\text{train}}^{40,45} $ $ -\!\!\!\!\lambda_{\text{train}}^{45，50} $ $ -\!\!\!\!\lambda_{\text{train}}^{40,50} $ $ -\!\!\!\!\lambda_{\text{train}}^{\mathrm{all}} $ SSIM 均值↑ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ MiniEnt - - - - - - - - - 0.911 0.924 0.921 0.919 CV-GMTINet 0.935 0.921 0.909 0.938 0.927 0.919 0.942 0.927 0.920 0.936 0.923 0.912 0.926 CV-CFUNet 0.955 0.942 0.924 0.954 0.935 0.932 0.954 0.934 0.931 0.959 0.947 0.936 0.942 CVPHD 0.981 0.977 0.939 0.940 0.979 0.974 0.981 0.944 0.972 0.978 0.973 0.969 0.967 DDPM 0.923 0.903 0.912 0.907 0.918 0.925 0.922 0.905 0.910 0.937 0.949 0.936 0.921 所提方法 0.979 0.973 0.949 0.964 0.972 0.968 0.977 0.956 0.965 0.981 0.975 0.966 0.969 注：MiniEnt为传统的最小熵重聚焦方法，无需进行训练。
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表 4 不同重聚焦方法PSNR指标结果(dB)
Table 4. PSNR results of different refocusing methods (dB)
重聚焦
方法$ -\!\!\!\!\lambda_{\text{train}}^{40,45} $ $ -\!\!\!\!\lambda_{\text{train}}^{45，50} $ $ -\!\!\!\!\lambda_{\text{train}}^{40,50} $ $ -\!\!\!\!\lambda_{\text{train}}^{\mathrm{all}} $ PSNR 均值↑ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ $ -\!\!\!\!\lambda_{\text{test}}^{40} $ $ -\!\!\!\!\lambda_{\text{test}}^{45} $ $ -\!\!\!\!\lambda_{\text{test}}^{50} $ MiniEnt - - - - - - - - - 27.868 28.793 28.542 28.401 CV-GMTINet 30.123 28.663 28.171 29.709 28.346 28.091 29.701 27.815 27.978 30.047 28.800 28.379 28.819 CV-CFUNet 30.471 29.312 27.628 30.637 27.704 28.979 30.740 27.773 28.854 31.369 29.908 29.559 29.411 CVPHD 34.377 34.548 28.974 28.720 34.548 34.745 34.008 28.525 33.191 33.013 32.140 31.782 32.381 DDPM 30.743 30.566 31.253 30.855 30.669 30.352 30.666 30.484 29.132 31.815 32.640 31.318 30.874 所提方法 35.290 33.920 30.521 32.521 33.553 33.611 35.173 30.918 33.106 36.251 34.309 33.042 33.518 注：MiniEnt为传统的最小熵重聚焦方法，无需进行训练。
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表 6 不同重聚焦方法在部件不同转速条件下的定量指标的均值
Table 6. Mean quantitative indicators of different refocusing methods under different rotational speeds of components
重聚焦方法 MAE ↓ SSIM ↑ PSNR(dB) ↑ 图像熵(bit) ↓ MiniEnt 1.468 0.914 27.815 8.679 CV-GMTINet 1.206 0.922 28.962 7.312 CV-CFUNet 0.999 0.945 29.825 6.769 CVPHD 0.974 0.954 30.794 6.413 DDPM 0.982 0.917 29.452 6.554 所提方法 0.931 0.957 32.410 5.128 注：表内加粗数值表示最优。
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表 7 消融实验定量指标的均值
Table 7. Mean quantitative indicators for the ablation experiment
方法 模块/损失 MAE ↓ SSIM ↑ PSNR(dB)↑ 图像熵(bit) ↓ 空频特征引导模块 像素损失 模型A × √ 0.769 0.966 32.877 5.138 模型B √ × 0.797 0.966 32.505 5.079 所提方法 √ √ 0.742 0.969 33.518 4.798 注：表内加粗数值表示最优。
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表 8 方法复杂度与推理效率对比
Table 8. Comparison of methods complexity and inference efficiency
方法 参数量(M) CPU单帧推理时间(s) GPU单帧推理时间(s) MiniEnt - 5.34±0.35 6.47±0.47 CV-GMTINet 12.6 18.87±0.21 0.86±0.08 CV-CFUNet 10.9 17.43±0.19 0.68±0.07 CVPHD 7.8 9.23±0.13 0.47±0.05 DDPM 15.3 169.24±0.53 1.09±0.12 所提方法 18.2 189.34±0.67 1.22±0.09
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