{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T00:41:59Z","timestamp":1776732119565,"version":"3.51.2"},"reference-count":35,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U22B2018"],"award-info":[{"award-number":["U22B2018"]}]},{"name":"National Natural Science Foundation of China","award":["62276205"],"award-info":[{"award-number":["62276205"]}]},{"name":"National Natural Science Foundation of China","award":["2022KXJ-157"],"award-info":[{"award-number":["2022KXJ-157"]}]},{"name":"Qin chuangyuan \u201cScientist + Engineer\u201d Team Construction Project of Shaanxi Province","award":["U22B2018"],"award-info":[{"award-number":["U22B2018"]}]},{"name":"Qin chuangyuan \u201cScientist + Engineer\u201d Team Construction Project of Shaanxi Province","award":["62276205"],"award-info":[{"award-number":["62276205"]}]},{"name":"Qin chuangyuan \u201cScientist + Engineer\u201d Team Construction Project of Shaanxi Province","award":["2022KXJ-157"],"award-info":[{"award-number":["2022KXJ-157"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Specific emitter identification (SEI) is a promising physical-layer authentication technique that serves as a crucial complement to upper-layer authentication mechanisms. SEI capitalizes on the inherent radio frequency fingerprints stemming from circuit discrepancies, which are intrinsic hardware properties and challenging to counterfeit. Recently, various deep learning (DL)-based SEI methods have been proposed, achieving outstanding performance. However, collecting and annotating substantial data for novel or unknown radiation sources is not only time-consuming but also cost-intensive. To address this issue, this paper proposes a few-shot (FS) metric learning-based time-frequency fusion network. To enhance the discriminative capability for radiation source signals, the model employs a convolutional block attention module (CBAM) and feature transformation to effectively fuse the raw signal\u2019s time domain and time-frequency domain representations. Furthermore, to improve the extraction of discriminative features under FS scenarios, the proxy-anchor loss and center loss are introduced to reinforce intra-class compactness and inter-class separability. Experiments on the ADS-B and Wi-Fi datasets demonstrate that the proposed TFAF-Net consistently outperforms existing models in FS-SEI tasks. On the ADS-B dataset, TFAF-Net achieves a 9.59% higher accuracy in 30-way 1-shot classification compared to the second-best model, and reaches an accuracy of 85.02% in 10-way classification. On the Wi-Fi dataset, TFAF-Net attains 90.39% accuracy in 5-way 1-shot classification, outperforming the next best model by 6.28%, and shows a 13.18% improvement in 6-way classification.<\/jats:p>","DOI":"10.3390\/rs16244635","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T06:44:05Z","timestamp":1733899445000},"page":"4635","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Few-Shot Metric Learning with Time-Frequency Fusion for Specific Emitter Identification"],"prefix":"10.3390","volume":"16","author":[{"given":"Shiyuan","family":"Mu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"},{"name":"54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China"}]},{"given":"Yong","family":"Zu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Shuai","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1480-9033","authenticated-orcid":false,"given":"Shuyuan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7372-9180","authenticated-orcid":false,"given":"Zhixi","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Junyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, P., Guo, L., Zhao, H., Shang, P., Chu, Z., and Lu, X. 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