低干信比SAR非虚假目标类有源干扰的轻量化鉴别网络

王重淞; 蒲巍; 高杰; 毛德庆; 刘欣远; 武俊杰; 黄钰林; 杨建宇

doi:10.12000/JR25195

低干信比SAR非虚假目标类有源干扰的轻量化鉴别网络

DOI: 10.12000/JR25195 CSTR: 32380.14.JR25195

电子科技大学信息与通信工程学院成都 611731

基金项目: 国家自然科学基金(U25B2015)

详细信息

作者简介:
王重淞，博士生，主要研究方向为合成孔径雷达抗干扰和深度学习等

蒲　巍，博士，教授，主要研究方向为合成孔径雷达成像、雷达信号处理、智能信号处理等

高　杰，硕士生，主要研究方向为智能化合成孔径雷达干扰鉴别和抑制

毛德庆，博士，副教授，主要研究方向为雷达探测成像、雷达超分辨成像、雷达智能化成像等

刘欣远，博士生，主要研究方向为智能化雷达抗干扰等

武俊杰，博士，教授，主要研究方向为双/多基SAR、智能化雷达成像等

黄钰林，博士，教授，主要研究方向为雷达成像、检测与识别和机器学习等

杨建宇，博士，教授，主要研究方向为前视雷达成像、双/多基SAR成像、新体制雷达探测与成像等

通讯作者:
蒲巍 puwei@uestc.edu.cn

责任主编：孙光才 Corresponding Editor: SUN Guangcai

中图分类号: TN958
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出版历程
- 收稿日期: 2025-09-29
- 修回日期: 2025-12-10
- 网络出版日期: 2025-12-29

Lightweight Discrimination Network for Non-spoofing Active Jamming in SAR under Low JSR

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (U25B2015)

More Information

Corresponding author: PU Wei, puwei@uestc.edu.cn

摘要

摘要: 合成孔径雷达(SAR)以其全天时、全天候及高分辨成像优势，在军事侦察与遥感监测等领域发挥着关键作用。然而，现代复杂电磁环境中的多样化干扰手段，严重破坏SAR回波信号特征，导致成像结果出现模糊、失真乃至目标完全不可辨识等问题。鉴于不同类型干扰在形成机理和抑制策略上的本质差异，精确的干扰鉴别成为实现有效抗干扰的核心前提。当前SAR干扰鉴别方法仍面临两大挑战：其一，在干扰信号与目标信号能量相近时，干扰特征易被目标能量掩盖，难以可靠检测与鉴别；其二，现有鉴别网络普遍复杂度过高、实时性差，难以满足实际工程应用需求。针对上述问题，该文提出一种基于轻量化网络的低干信比SAR非虚假目标类有源干扰鉴别网络。该方法创新性地引入了格型变换模块与超核感知模块。其中，格型变换模块旨在强化对干扰目标的细粒度特征提取能力，进而显著提升了低干信比条件下的干扰鉴别性能；超核感知模块则基于点目标成像特性，设计了超核卷积，该卷积在增强上下文信息捕获能力的同时，亦实现了算法的轻量化。实验部分通过多维度评估验证了方法的优越性，包括模块的有效性分析、不同模型的精度-复杂度权衡分析，以及不同干信比下的鲁棒性测试。结果表明，所提方法在低干信比条件下仍能保持较高鉴别性能，同时计算效率满足实时性需求。
- 合成孔径雷达 /
- 有源干扰 /
- 轻量化 /
- 卷积神经网络 /
- 鉴别
Abstract: Synthetic Aperture Radar (SAR) plays a pivotal role in military reconnaissance and remote-sensing applications, given its all-weather, day-and-night operability and high-resolution imaging performance. However, diverse jamming techniques in modern complex electromagnetic environments severely distort SAR echo signals, leading to blurred or distorted imaging results and, in extreme cases, complete target unrecognizability. Given the fundamental differences in formation mechanisms and suppression strategies of different jamming types, precise jamming identification is a core prerequisite for effective counterjamming. Current SAR jamming identification methods face two major challenges. First, when the energy of the jamming signal is comparable to that of the target signal, the jamming features are easily masked, making reliable detection and identification difficult. Second, existing identification networks generally suffer from excessive complexity and poor real-time performance, limiting their practicality in engineering applications. To address these issues, this paper proposes a lightweight network-based non-spoofing active jamming identification method for SAR under low Jamming-to-Signal Ratio (JSR) conditions. This method introduces two key components: a lattice transform block that boosts interference discrimination at low JSR by refining fine-grained feature extraction and a hyperkernel-aware module that, through a custom hyperkernel block based on point target imaging, enhances context capture while ensuring algorithmic lightweighting. The superiority of the proposed method is validated through multidimensional evaluations, including effectiveness analyses of the modules, accuracy-complexity trade-off analysis of different models, and robustness testing under varying JSR conditions. The proposed method maintains high identification performance even under low JSR conditions while meeting real-time computational efficiency requirements.
- Synthetic Aperture Radar (SAR) /
- Active jamming /
- Lightweight /
- Convolutional Neural Network (CNN) /
- Discrimination

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图 1 不同干扰功率的成像结果示意图

Figure 1. Illustration of imaging results under different interference powers

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图 2 SAR系统几何构型

Figure 2. SAR system geometry

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图 3 间歇采样转发类干扰的不同策略(a) 一个合成孔径回波；(b) 直接转发；(c) 重复转发；(d) 循环转发

Figure 3. Implementation approaches of sampled-repeater jamming (a) Synthetic aperture echo; (b) ISRJ; (c) ISRRJ; (d) ISCRJ

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图 4 不同干扰样式的干扰下成像结果

Figure 4. Imaging results under different jamming

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图 5 不同JSR下的压制干扰结果对比图

Figure 5. Comparative diagram of suppression interference results under different JSR conditions

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图 6 不同JSR下的转发类干扰结果对比图

Figure 6. Comparative diagram of forwarding class interference results under different JSR conditions

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图 7 轻量化有源干扰鉴别网络结构

Figure 7. Lightweight network architecture for active jamming recognition

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图 8 所提格型变换模块结构

Figure 8. The proposed structure of lattice transform block

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图 9 $ H(\cdot ) $ 的卷积结构

Figure 9. CNN block of $ H(\cdot ) $

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图 10 SAR点目标成像结果

Figure 10. SAR point target imaging

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图 11 超核卷积的结构

Figure 11. Structure of the Hyperkernel Block

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图 12 数据集仿真流程图

Figure 12. Flowchart of dataset simulation

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图 13 无干扰下的干扰数据集数据切片

Figure 13. Dataset samples without jamming

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图 14 模型训练过程损失曲线

Figure 14. Loss value of models in training process

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图 15 不同结构的热力图(Grad-CAM)对比结果(第1行展示了没有使用格型变换模块的可视化，第2行展示了带有格型变换模块的可视化)

Figure 15. Comparison of heatmap (Grad-CAM) results from different structures (the first row displays visualizations without the LT Block, while the second row presents corresponding visualizations incorporating the LT Block)

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图 16 所提方法鉴别结果的混淆矩阵

Figure 16. The confusion matrix of the discrimination results of the proposed method

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图 17 不同JSR下的模型性能

Figure 17. Model performance under different JSR conditions

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图 18 哨兵1号实测数据

Figure 18. Sentinel-1 measured data

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表 1 干扰样式编码

Table 1. Jamming coding

干扰类型	编号	干扰类型	编号
无干扰	C0	方位向均匀间歇采样转发干扰	C1
方位向非均匀间歇采样转发干扰	C2	距离向均匀间歇采样转发干扰	C3
距离向非均匀间歇采样转发干扰	C4	二维均匀间歇采样转发干扰	C5
二维非均匀间歇采样转发干扰	C6	均匀间歇采样重复转发干扰	C7
非均匀间歇采样循环转发干扰	C8	乘性调制噪声干扰	C9
步进移频干扰	C10	分段移频干扰	C11
随机移频干扰	C12	失配干扰	C13
噪声压制干扰	C14	一维卷积噪声干扰	C15
二维卷积噪声干扰	C16	窄带射频干扰	C17
正弦调频宽带射频干扰	C18	线性调频宽带射频干扰	C19

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表 2 消融实验结果(%)

Table 2. Ablation study results (%)

模型结构	OA	AP	AR	F1
基线模型	85.66	84.44	85.98	85.20
基线模型+超核卷积模块	91.44	93.90	95.25	94.57
基线模型+格型变换模块+ 超核卷积模块	99.57	99.56	99.63	99.60

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表 3 各种鉴别方法的准确率性能(%)

Table 3. Comparison of accuracy for various discrimination methods (%)

编号	VGG-16	ResNet-18	Inception v4	Xception	Mobilenet V2	VGG-16+AN	Ours
C0	99.12	98.69	96.78	94.96	93.06	99.42	100.00
C1	100.00	93.89	100.00	93.45	96.33	99.13	100.00
C2	91.27	95.63	90.39	84.72	96.33	100.00	100.00
C3	96.07	92.14	98.69	84.28	94.50	99.13	99.13
C4	90.39	74.24	59.83	74.22	80.73	98.25	98.25
C5	96.94	90.83	96.07	73.36	86.24	94.76	100.00
C6	100.00	96.07	98.25	99.13	95.41	97.82	100.00
C7	97.82	60.26	96.94	94.76	69.72	99.13	99.13
C8	82.10	78.17	86.03	76.86	74.31	95.20	100.00
C9	96.51	95.20	99.56	95.63	93.58	96.07	98.25
C10	82.53	86.03	82.53	79.91	85.32	84.72	96.51
C11	100.00	100.00	99.56	100.00	100.00	100.00	100.00
C12	94.32	90.39	93.45	88.21	94.50	98.25	100.00
C13	100.00	100.00	100.00	98.69	100.00	100.00	100.00
C14	100.00	99.56	89.08	99.56	99.08	100.00	100.00
C15	100.00	95.63	100.0	98.25	96.33	100.00	100.00
C16	100.00	99.13	95.20	82.53	98.17	99.56	100.00
C17	100.00	99.13	100.00	100.00	98.17	100.00	100.00
C18	100.00	98.25	99.56	98.69	98.17	100.00	100.00
C19	100.00	99.56	97.82	95.63	99.08	100.00	100.00
注：加粗数值表示最优。

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表 4 各种鉴别方法的性能指标

Table 4. Performance metrics of various discrimination models

指标	VGG-16	ResNet-18	Inception v4	Xception	Mobilenet V2	VGG-16+AN	Ours
OA(%)	96.42	92.35	94.05	90.76	92.51	98.10	99.57
AP(%)	96.35	92.14	93.99	90.64	92.56	98.07	99.56
AR(%)	96.84	92.48	94.92	91.79	92.45	99.42	99.63
F1 (%)	96.66	92.31	93.99	91.21	92.51	98.24	99.60
Kappa (%)	96.23	91.94	93.74	90.26	92.04	98.00	99.55
Flops(G)	80.7	9.5	37.8	2.4	1.7	365	3.5
Params(M)	134	11.19	41.17	20.85	2.25	2338.06	4.32
TPI(s)	0.51	0.09	0.47	0.42	0.15	3.44	0.21
注：加粗数值表示最优。

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