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A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network

文献类型：期刊论文

作者Liu, Pengfei1,2,3; Guo, Lishu1,3; Zhao, Hang1,3; Shang, Peng1,3; Chu, Ziyue1,2,3; Lu, Xiaochun1,2,3
刊名SENSORS
出版日期2023-05-12
卷号23期号:10页码:25
关键词space radiation source closed set recognition open set recognition prototype learning
DOI10.3390/s23104708
英文摘要As a basic task and key link of space situational awareness, space target recognition has become crucial in threat analysis, communication reconnaissance and electronic countermeasures. Using the fingerprint features carried by the electromagnetic signal to recognize is an effective method. Because traditional radiation source recognition technologies are difficult to obtain satisfactory expert features, automatic feature extraction methods based on deep learning have become popular. Although many deep learning schemes have been proposed, most of them are only used to solve the inter-class separable problem and ignore the intra-class compactness. In addition, the openness of the real space may invalidate the existing closed-set recognition methods. In order to solve the above problems, inspired by the application of prototype learning in image recognition, we propose a novel method for recognizing space radiation sources based on a multi-scale residual prototype learning network (MSRPLNet). The method can be used for both the closed- and open-set recognition of space radiation sources. Furthermore, we also design a joint decision algorithm for an open-set recognition task to identify unknown radiation sources. To verify the effectiveness and reliability of the proposed method, we built a set of satellite signal observation and receiving systems in a real external environment and collected eight Iridium signals. The experimental results show that the accuracy of our proposed method can reach 98.34% and 91.04% for the closed- and open-set recognition of eight Iridium targets, respectively. Compared to similar research works, our method has obvious advantages.
WOS关键词MODE DECOMPOSITION ; RADIO
资助项目Technical Support Talent Plan of Chinese Academy of Science[E317YR17] ; Project for Guangxi Science and Technology Base and Talents[GK AD22035957] ; National Natural Science Foundation of China[12273045] ; Western Talent Introduction Project of Chinese Academy of Sciences[E016YR1R] ; High Level Talent Project of Shaanxi Province[E039SB1K]
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
出版者MDPI
WOS记录号WOS:000996836300001
资助机构Technical Support Talent Plan of Chinese Academy of Science ; Technical Support Talent Plan of Chinese Academy of Science ; Project for Guangxi Science and Technology Base and Talents ; Project for Guangxi Science and Technology Base and Talents ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Western Talent Introduction Project of Chinese Academy of Sciences ; Western Talent Introduction Project of Chinese Academy of Sciences ; High Level Talent Project of Shaanxi Province ; High Level Talent Project of Shaanxi Province ; Technical Support Talent Plan of Chinese Academy of Science ; Technical Support Talent Plan of Chinese Academy of Science ; Project for Guangxi Science and Technology Base and Talents ; Project for Guangxi Science and Technology Base and Talents ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Western Talent Introduction Project of Chinese Academy of Sciences ; Western Talent Introduction Project of Chinese Academy of Sciences ; High Level Talent Project of Shaanxi Province ; High Level Talent Project of Shaanxi Province ; Technical Support Talent Plan of Chinese Academy of Science ; Technical Support Talent Plan of Chinese Academy of Science ; Project for Guangxi Science and Technology Base and Talents ; Project for Guangxi Science and Technology Base and Talents ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Western Talent Introduction Project of Chinese Academy of Sciences ; Western Talent Introduction Project of Chinese Academy of Sciences ; High Level Talent Project of Shaanxi Province ; High Level Talent Project of Shaanxi Province ; Technical Support Talent Plan of Chinese Academy of Science ; Technical Support Talent Plan of Chinese Academy of Science ; Project for Guangxi Science and Technology Base and Talents ; Project for Guangxi Science and Technology Base and Talents ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Western Talent Introduction Project of Chinese Academy of Sciences ; Western Talent Introduction Project of Chinese Academy of Sciences ; High Level Talent Project of Shaanxi Province ; High Level Talent Project of Shaanxi Province
源URL[http://210.72.145.45/handle/361003/14229]  
专题国家授时中心_导航与通信研究室
通讯作者Guo, Lishu
作者单位1.Chinese Acad Sci, Key Lab Precise Positioning & Timing Technol, Xian 710600, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Natl Time Serv Ctr, Xian 710600, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Pengfei,Guo, Lishu,Zhao, Hang,et al. A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network[J]. SENSORS,2023,23(10):25.
APA Liu, Pengfei,Guo, Lishu,Zhao, Hang,Shang, Peng,Chu, Ziyue,&Lu, Xiaochun.(2023).A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network.SENSORS,23(10),25.
MLA Liu, Pengfei,et al."A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network".SENSORS 23.10(2023):25.

入库方式： OAI收割

来源：国家授时中心

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