Radar small sample target recognition method based on meta learning and its improvement

被引：0
|
作者：
Sun J. [1 ,2 ]
Yu S. [1 ,2 ]
Sun 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 | 2022年 / 44卷 / 06期
关键词：
High resolution range profile (HRRP); Meta learning; Radar target recognition; Small sample recognition; Transfer learning;
D O I：
10.12305/j.issn.1001-506X.2022.06.09
中图分类号：
学科分类号：
摘要：
In recent years, radar target recognition based on deep learning has received great attention. However, there are time constraints and resource constraints in actual combat, and the problem of small sample recognition greatly limits its performance in actual recognition tasks. To solve this problem, based on the meta learning algorithm, this paper improves the performance of new tasks by learning meta knowledge from multiple related tasks, introduces the idea of transfer learning, proposes an improved small sample learning method, and analyzes the application boundary conditions of the method through detailed performance comparison experiments. The experimental results based on the measured high resolution range profile data show that the performance advantage of meta learning method is outstanding only when there are many target categories in the historical accumulated samples and in the case of minimal samples with high correlation with the target task, the proposed method can significantly improve its comprehensive recognition performance. © 2022, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：1839 / 1845
页数：6
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