{"title":"基于ConvNeXt网络的半监督特定发射器识别方法","authors":"Dian Lv, Zhiyong Yu, Junjie Cao, Jiawei Xie","doi":"10.1049/ell2.70326","DOIUrl":null,"url":null,"abstract":"<p>To address the challenge of unknown emitter identification in communication source recognition, this study proposes a semi-supervised recognition framework. A multi-temporal-frequency feature fusion channel is constructed, and an attention-augmented ConvNeXt architecture is designed to process fused features. By freezing the deep features extracted from the pretrained closed-set network, unknown emitters are classified using the K-means algorithm. The frozen closed-set model achieves 90% emitter identification accuracy under low SNR conditions, with performance improvements ranging from 10% to 30% over conventional methods. Experimental validation on 3-class and 4-class unknown emitter datasets demonstrates up to a 50% recognition rate enhancement, substantiating the framework's efficacy in open-set emitter classification.</p>","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70326","citationCount":"0","resultStr":"{\"title\":\"Semi-Supervised Specific Emitter Identification Method Based on ConvNeXt Network\",\"authors\":\"Dian Lv, Zhiyong Yu, Junjie Cao, Jiawei Xie\",\"doi\":\"10.1049/ell2.70326\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>To address the challenge of unknown emitter identification in communication source recognition, this study proposes a semi-supervised recognition framework. A multi-temporal-frequency feature fusion channel is constructed, and an attention-augmented ConvNeXt architecture is designed to process fused features. By freezing the deep features extracted from the pretrained closed-set network, unknown emitters are classified using the K-means algorithm. The frozen closed-set model achieves 90% emitter identification accuracy under low SNR conditions, with performance improvements ranging from 10% to 30% over conventional methods. Experimental validation on 3-class and 4-class unknown emitter datasets demonstrates up to a 50% recognition rate enhancement, substantiating the framework's efficacy in open-set emitter classification.</p>\",\"PeriodicalId\":11556,\"journal\":{\"name\":\"Electronics Letters\",\"volume\":\"61 1\",\"pages\":\"\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2025-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70326\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electronics Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/ell2.70326\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronics Letters","FirstCategoryId":"5","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/ell2.70326","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Semi-Supervised Specific Emitter Identification Method Based on ConvNeXt Network
To address the challenge of unknown emitter identification in communication source recognition, this study proposes a semi-supervised recognition framework. A multi-temporal-frequency feature fusion channel is constructed, and an attention-augmented ConvNeXt architecture is designed to process fused features. By freezing the deep features extracted from the pretrained closed-set network, unknown emitters are classified using the K-means algorithm. The frozen closed-set model achieves 90% emitter identification accuracy under low SNR conditions, with performance improvements ranging from 10% to 30% over conventional methods. Experimental validation on 3-class and 4-class unknown emitter datasets demonstrates up to a 50% recognition rate enhancement, substantiating the framework's efficacy in open-set emitter classification.
期刊介绍:
Electronics Letters is an internationally renowned peer-reviewed rapid-communication journal that publishes short original research papers every two weeks. Its broad and interdisciplinary scope covers the latest developments in all electronic engineering related fields including communication, biomedical, optical and device technologies. Electronics Letters also provides further insight into some of the latest developments through special features and interviews.
Scope
As a journal at the forefront of its field, Electronics Letters publishes papers covering all themes of electronic and electrical engineering. The major themes of the journal are listed below.
Antennas and Propagation
Biomedical and Bioinspired Technologies, Signal Processing and Applications
Control Engineering
Electromagnetism: Theory, Materials and Devices
Electronic Circuits and Systems
Image, Video and Vision Processing and Applications
Information, Computing and Communications
Instrumentation and Measurement
Microwave Technology
Optical Communications
Photonics and Opto-Electronics
Power Electronics, Energy and Sustainability
Radar, Sonar and Navigation
Semiconductor Technology
Signal Processing
MIMO