SAR图像子带对比度和子带熵：概念及在干扰检测与抑制中的应用

杨会章; 席峰; 刘中; 刘爱芳; 杨健

doi:10.12000/JR25027

SAR图像子带对比度和子带熵：概念及在干扰检测与抑制中的应用

DOI: 10.12000/JR25027

杨会章^1, ,,
席峰¹,
刘中¹,
刘爱芳²,
杨健³

1.
南京理工大学电子工程与光电技术学院南京 210094
2.
南京电子技术研究所南京 210039
3.
清华大学电子工程系北京 100084

基金项目: 国家自然科学基金(62301259, 62171224)，中央高校基本科研业务费专项资金项目(30924010914)

详细信息

作者简介:
杨会章，博士，副教授，主要研究方向为雷达信号与图像处理

席　峰，博士，副教授，主要研究方向雷达信号处理、统计和阵列信号处理、雷达通信一体化

刘　中，博士，教授，主要研究方向雷达系统、雷达信号处理、阵列信号处理

刘爱芳，博士，研究员，主要研究方向为合成孔径雷达总体设计

杨　健，博士，教授，主要研究方向为极化雷达理论及其应用等

通讯作者:
杨会章 hzyang@njust.edu.cn

责任主编：李永祯 Corresponding Editor: LI Yongzhen
1¹⁾ https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.betainc.html²⁾ https://ww2.mathworks.cn/help/stats/betainv.html
中图分类号: TN972
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出版历程
- 收稿日期: 2025-04-12
- 修回日期: 2025-05-21

Sub-band Contrast and Entropy of SAR Images: Concepts and Applications to Interference Detection and Suppression

1.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2.
Nanjing Research Institute of Electronics Technology, Nanjing 210039, China
3.
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Funds: The National Natural Science Foundation of China (62301259, 62171224), The Fundamental Research Funds for the Central Universities (30924010914)

More Information

Corresponding author: YANG Huizhang, hzyang@njust.edu.cn

摘要

摘要: 该文提出一种从单视复数合成孔径雷达(SAR)图像中检测干扰的方法，并将该方法用于自适应干扰抑制处理。所提方法不仅适用于间歇采样转发干扰，还适用于星载SAR中常见的线性调频脉冲等无意干扰。首先将一幅单视复数SAR图像在距离频域按照等带宽划分为多个子带图像，然后对子带图像的像素强度进行建模，并分析干扰像素和非干扰像素在子带域的起伏机制。干扰像素在不同子带中的能量分布不均匀，导致其强度在子带域具有显著的起伏，而非干扰像素的强度在子带域则较为稳定。基于上述发现，本文定义子带对比度和子带熵作为统计量以衡量像素强度在子带域的起伏特征，然后把两者与设定的阈值进行比较以得到干扰检测结果。经过统计分析，在无干扰情况下上述两个统计量近似服从Beta分布。基于这一发现，本文用Beta分布拟合这两者的分布，并在恒虚警准则下给出了检测阈值的确定方法。实验结果表明，所提方法不仅能有效检测间歇采样转发干扰，还能检测常见的无意干扰。本文还研究了干信比对检测性能的影响，并通过蒙特卡罗仿真验证了方法的可靠性和稳定性。此外，该文还提出了一种基于秩1模型的干扰抑制方法，能够对被检测到含有干扰的图像区域进行自适应干扰抑制，从而降低干扰对下游任务的不利影响。
- 合成孔径雷达 /
- 有源干扰 /
- 干扰检测 /
- 干扰抑制 /
- 干扰解译 /
- 频谱分析 /
- 统计分析
Abstract: Spaceborne Synthetic Aperture Radar (SAR) data may be prone to interrupted-sampling repeater jamming and many common unintentional interferences, such as linear frequency modulated pulses. In this paper, we first divide a single-look complex SAR image into multiple subband images of equal bandwidth in the range frequency domain. Then, we model the pixel intensity of these subband images and analyze the fluctuation mechanism of interfering and noninterfering pixels across the subbands. The findings reveal that the energy distribution of interfering pixels is uneven across different subbands, leading to substantial intensity fluctuations within the subband domain, whereas the intensity of noninterfering pixels remains relatively stable. Based on this observation, we define subband contrast and subband entropy as statistical measures to quantify fluctuation characteristics across the subbands. These measures are then compared with certain thresholds to obtain detection results. Statistical analysis revealed that under noninterfering conditions, these two statistics approximately follow the beta distribution. By leveraging this finding, we fit the distributions of these measures using the beta distribution and develop a method to determine detection thresholds under the constant-false-alarm-rate criterion. Experimental results showed that the proposed method can effectively detect interrupted-sampling repeater jamming and common unintentional interferences. In addition, we investigated the impact of the jamming-to-signal ratio on detection performance and verified the reliability and stability of the method via Monte Carlo simulations. Furthermore, we introduced an interference suppression technique based on a rank-1 model to reduce the adverse effects of interference on downstream tasks. This technique is capable of adaptively suppressing interference in detected regions.
- Synthetic Aperture Radar (SAR) /
- Active interference /
- Interference detection /
- Interference suppression /
- Interference interpretation /
- Spectral analysis /
- Statistical analysis

HTML全文

图 1 频谱均衡示意图

Figure 1. Illustration of spectrum equalization

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图 2 子带图像生成流程图

Figure 2. Diagram of subband image generation

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图 3 SAR图像间歇采样转发干扰示例与干扰的时频图

Figure 3. Example ISRJ in a SAR image and its spectrogram

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图 4 一幅含间歇采样转发干扰的SAR图像所生成的10幅子带图像

Figure 4. Ten subband images generated from a SAR image having ISRJ

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图 5 强目标像素(P1-P3)、背景像素(P4-P6)、干扰像素(P7-P9)的子带域起伏曲线

Figure 5. The fluctuation profiles of strong pixels (P1-P3), background pixels (P4-P6), and interference pixels (P7-P9)

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图 6 一幅单视复SAR图像计算得到的子带对比度和子带熵示例

Figure 6. Examples of subband contrast and subband entropy for a SAR image with ISRJ

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图 7 子带对比度和子带熵样本的直方图分布和Beta分布概率密度函数拟合结果示例

Figure 7. Example of histogram distribution and beta fitting results of subband contrast and subband entropy

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图 8 子带对比度和子带熵样本的累计直方图分布和Beta分布累计概率密度函数拟合结果示例

Figure 8. Example of cumulative histogram and beta fitting results for subband contrast and subband entropy

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图 9 基于子带对比度/熵的干扰检测与自适应干扰抑制流程

Figure 9. Interference detection and adaptive interference suppression flow based on subband contrast/entropy

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图 10 第1组实验的4种干扰情况和检测与抑制结果

Figure 10. Four interference conditions and detection and suppression results of the first group of experiments

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图 11 第2组实验的四种干扰情况和检测与抑制结果

Figure 11. Four interference conditions and detection and suppression results of the second group of experiments

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图 12 第3组实验的四种干扰情况和检测与抑制结果

Figure 12. Four interference conditions and detection and suppression results of the third group of experiments

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图 13 第四组实验的干扰检测与抑制结果

Figure 13. Four interference conditions and detection and suppression results of the fourth group of experiments

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图 14 第4组实验中无意干扰的子带对比度特征图(第2行)和子带熵特征图(第3行)

Figure 14. Subband contrast feature maps (row 2) and subband entropy feature maps (row 3) in the fourth group of experiments

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图 15 两组干扰像素的子带对比度和子带熵随JSR的变化曲线

Figure 15. Curves of subband contrast and subband entropy for two groups of interfering pixels with JSR

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图 16 用子带对比度进行检测获得的检测概率随阈值和虚警概率的变化曲线

Figure 16. Curves of detection probability obtained by detection using subband contrast under different thresholds and false alarm probabilities

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图 17 用子带熵进行检测获得的检测概率随检测阈值和虚警概率的变化曲线

Figure 17. Curves of detection probability obtained by detection using subband entropy under different thresholds and false alarm probabilities

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