雷达学报2022，Vol.11Issue(5)：897-908,12.DOI:10.12000/JR22143
基于深度CNN模型的SAR图像有源干扰类型识别方法
SAR Image Active Jamming Type Recognition Based on Deep CNN Model
摘要
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.关键词
合成孔径雷达/有源干扰/深度学习/注意力机制/识别Key words
Synthetic Aperture Radar (SAR)/ Active jamming/ Deep learning/ Attention mechanism/Recognition分类
信息技术与安全科学引用本文复制引用
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):897-908,12.基金项目
国家自然科学基金(62122091, 61771480)，湖南省自然科学基金(2020JJ2034) （62122091, 61771480）
