雷达小样本目标识别方法及应用分析  

Radar few shot target recognition method and application analysis

作　　者：晏媛 孙俊[1,2] 孙晶明[1,2] 于俊朋[1,2] YAN Yuan;SUN Jun;SUN Jingming;YU Junpeng(Nanjing Research Institute of Electronics Technology, Nanjing 210039, China;Key Laboratory of IntelliSense Technology, China Electronics Technology Group Corporation, Nanjing 210039, China)

机构地区：[1]南京电子技术研究所,江苏南京210039 [2]中国电子科技集团公司智能感知技术重点实验室,江苏南京2100039

出　　处：《系统工程与电子技术》2021年第3期684-692,共9页Systems Engineering and Electronics

基　　金：国家自然科学基金(U19B2031)资助课题。

摘　　要：针对雷达小样本目标识别问题,结合元学习和迁移学习提出一套综合解决方案,旨在根据实际应用场景的不同提供合适的模型学习方式和分类方式,从而提升雷达小样本目标识别效率和准确率。同时,通过多组对比实验深入分析小样本学习算法在实际雷达目标识别场景下的模型性能变化,得出两个可有效指导工程化应用的重要结论。元学习模型在源任务信息充足且源任务与目标任务间差异性小时性能表现良好,否则迁移学习方法更适用;小样本学习模型对雷达目标外在特征的关注度不同,以识别为目的的雷达成像应重点关注模型需求的显著性特征。Aiming at the problem of radar small shot target recognition,a comprehensive solution is proposed by combining meta learning and transfer learning,to provide appropriate model learning and classification methods according to different practical application scenarios,so as to improve the efficiency and accuracy of radar small shot target recognition.At the same time,through several groups of comparative experiments,the model performance changes of few shot learning algorithm in the actual radar target recognition scene are deeply analyzed,and two important conclusions that can effectively guide the engineering application are obtained.One is the performance of meta learning model is good when the source task information is sufficient and the difference between the source task and the target task is small,otherwise the transfer learning method is more suitable.The other one is the few shot learning model pay different attention to the external features of radar targets,so the recognition oriented radar imaging should focus on the salient features of the model requirements.

关 键 词：雷达目标识别 小样本学习 元学习 迁移学习 

分 类 号：TN911.73[电子电信—通信与信息系统]