| 基于深度CNN模型的SAR图像有源干扰类型识别方法 |
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| 引用本文： | 陈思伟,崔兴超,李铭典,陶臣嵩,李郝亮.基于深度CNN模型的SAR图像有源干扰类型识别方法[J].雷达学报,2022,11(5):897-908. |
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| 作者姓名： | 陈思伟 崔兴超 李铭典 陶臣嵩 李郝亮 |
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| 作者单位： | 国防科技大学电子科学学院电子信息系统复杂电磁环境效应国家重点实验室 长沙 410073 |
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| 基金项目： | 国家自然科学基金(62122091, 61771480)，湖南省自然科学基金(2020JJ2034) |
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| 摘 要： | 合成孔径雷达(SAR)能够全天时全天候获取感兴趣区域的高分辨率雷达图像，在诸多领域获得了成功应用。在电子对抗博弈环境下，SAR图像解译与情报生成也面临复杂电磁干扰的严重影响。当前，国内外学者提出了许多SAR抗干扰技术方法。然而，作为抗干扰的前提，SAR图像干扰类型识别这一关键技术却鲜有报道。该文针对SAR图像典型有源干扰类型识别开展研究。首先，选取5种典型有源干扰样式，并根据干扰参数，细分为9种干扰类型，作为干扰识别对象。其次，开展干扰信号回波仿真，通过与MiniSAR实测数据进行回波域叠加和成像处理，构建了典型有源干扰类型样本集。在此基础上，提出了一种结合注意力机制的深度卷积神经网络(CNN)模型，并开展了对比实验验证。实验表明，对不同场景和不同干扰参数情形，相比于传统深度CNN模型，该文方法取得了更高的识别精度和更稳健的性能。
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| 关 键 词： | 合成孔径雷达 有源干扰 深度学习 注意力机制 识别 |
| 收稿时间： | 2022-07-09 |
SAR Image Active Jamming Type Recognition Based on Deep CNN Model |
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| CHEN Siwei,CUI Xingchao,LI Mingdian,TAO Chensong,LI Haoliang.SAR Image Active Jamming Type Recognition Based on Deep CNN Model[J].Journal of Radars,2022,11(5):897-908. |
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| Authors: | CHEN Siwei CUI Xingchao LI Mingdian TAO Chensong LI Haoliang |
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| Affiliation: | The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science, National University of Defense Technology, Changsha 410073, China |
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| Abstract: | Synthetic Aperture Radar (SAR) can acquire high-resolution radar images of region of interest under all-day and all-weather conditions, a capability that has been successfully applied in many fields. In the environment of military confrontation games, complex electromagnetic jamming severely impacts SAR image interpretation and intelligence generation. Scholars have proposed numerous SAR anti-jamming approaches to date. However, the recognition of SAR image jamming types, which is the prerequisite of anti-jamming, has rarely been reported. This work focuses on active jamming type recognition in SAR images. First, five typical active jamming modes are selected and further subdivided into nine jamming types based on various jamming parameters, which serve as the objects of jamming recognition. The typical active jamming datasets are then constructed based on the stacking of simulated jamming signal echoes and real-measured MiniSAR data in the echo domain and SAR imaging processing. Based on the jamming datasets, an attention-combining deep Convolutional Neural Network (CNN) model has been proposed. Thereafter, comparative experiments are performed. Experiments show that, compared with traditional deep CNN models, the proposed method achieves more accurate recognition and more stable performance across various scenes and jamming parameter configurations. |
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