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

doi:10.3390/s23104708

NTSC-IR > 导航与通信研究室

	A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network
	Liu, Pengfei 1,2,3; Guo, Lishu 1,3; Zhao, Hang1,3 ; Shang, Peng 1,3; Chu, Ziyue 1,2,3; Lu, Xiaochun1,2,3
	2023-05-12
发表期刊	SENSORS
卷号	23 期号:10 页码:25
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.
关键词	space radiation source closed set recognition open set recognition prototype learning
资助者	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
DOI	10.3390/s23104708
关键词[WOS]	MODE DECOMPOSITION ; RADIO
语种	英语
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]
资助者	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
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000996836300001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://210.72.145.45/handle/361003/14229
专题	导航与通信研究室
通讯作者	Guo, Lishu
作者单位	1.Chinese Acad Sci, Natl Time Serv Ctr, Xian 710600, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Key Lab Precise Positioning & Timing Technol, 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.