Few-shot specific emitter identification using open-set recognition classifiers

Few-shot specific emitter identification using open-set recognition classifiers | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 29 January 2025 V1 Latest version Share on Few-shot specific emitter identification using open-set recognition classifiers Authors : Xuelin Yang 0000-0003-0197-7959 [email protected] , Mutala Mohammed , Zhi Chai , Minye Li , and Rahel Abayneh Authors Info & Affiliations https://doi.org/10.22541/au.173815769.93002929/v1 448 views 183 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Physical-layer specific emitter identification (SEI) faces significant misclassification challenges of unknown devices, especially in closed-set environments and the scarcity of available labeled data. To address these limitations, we introduce a Few-Spot Self-Supervised Adversarial Augmentation Specific Emitter Identification (FS-SA2SEI) framework that integrates adversarial augmentation, and open-set recognition (OSR) mechanisms. FS-SA2SEI enhances spectral and multi-resolution feature extraction while enabling robust classification of known emitters and rejection of unknown ones. Evaluated on Wi-Fi and publicly available datasets (ADS-B, FIT/CorteXlab), the framework achieves a closed-set accuracy of 89.9%, surpassing prior benchmarks by >5%, and an open-set detection rate (OSDR) of 64.0% at a worst-case openness of 0.6. These results highlight FS-SA2SEI’s potential as a scalable solution for real-time SEI in resource-constrained IoT systems, where adaptability to dynamic environments and unseen devices is critical. Supplementary Material File (1.manuscript_fs-sa2sei_osr.docx) Download 463.44 KB Information & Authors Information Version history V1 Version 1 29 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords computer network security fingerprint identification radiofrequency identification time-frequency analysis wavelet transforms wireless communications Authors Affiliations Xuelin Yang 0000-0003-0197-7959 [email protected] Shanghai Jiao Tong University View all articles by this author Mutala Mohammed Shanghai Jiao Tong University View all articles by this author Zhi Chai Shanghai Jiao Tong University View all articles by this author Minye Li Shanghai Jiao Tong University View all articles by this author Rahel Abayneh Shanghai Jiao Tong University View all articles by this author Metrics & Citations Metrics Article Usage 448 views 183 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xuelin Yang, Mutala Mohammed, Zhi Chai, et al. Few-shot specific emitter identification using open-set recognition classifiers. Authorea . 29 January 2025. 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