Radar few shot target recognition method and application analysis

被引：0
|
作者：
Yan Y. [1 ,2 ]
Sun J. [1 ,2 ]
Sun J. [1 ,2 ]
Yu J. [1 ,2 ]
机构：
[1] Nanjing Research Institute of Electronics Technology, Nanjing
[2] Key Laboratory of IntelliSense Technology, China Electronics Technology Group Corporation, Nanjing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 03期
关键词：
Few shot learning; Meta learning; Radar target recognition; Transfer learning;
D O I：
10.12305/j.issn.1001-506X.2021.03.11
中图分类号：
学科分类号：
摘要：
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. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：684 / 692
页数：8
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