小样本通信辐射源个体识别研究进展

doi:10.12305/j.issn.1001-506X.2026.09.35

摘要/Abstract

摘要：

通信辐射源个体识别（specific emitter identification，SEI）通过提取反映辐射源个体差异的射频指纹特征，实现对不同辐射源个体的识别。近年来，基于深度学习的通信SEI受到日益广泛的关注。然而，在非合作场景下，基于深度学习的通信SEI面临数据集获取难度大、标注成本高的挑战。因此，基于小样本学习的SEI已成为研究的热点。在讨论了射频指纹产生机理和基于深度学习的SEI系统模型的基础上，进一步分类评述了小样本通信SEI方法，并讨论其轻量化部署。最后，总结了未来通信SEI的研究趋势，期望能为相关领域的发展提供帮助。

关键词: 通信辐射源个体识别, 射频指纹, 小样本, 自监督学习, 半监督学习

Abstract:

Communication specific emitter identification （SEI） identifies emitters by extracting radio frequency fingerprints from emitter signals that reflect their individual differences. With the rapid advancement of deep learning, deep learning-based communication SEI attracts increasing attention. However, in non-cooperative scenarios, such methods encounter significant challenges, including difficulties in data acquisition and high annotation costs. Consequently, few-shot communication SEI emerges as a prominent research focus. Different categories of few-shot communication SEI methods and their lightweight deployments are elaborated herein, upon the discussion of the radio frequency fingerprint formation mechanism and system model of deep learning-based communication SEI. Finally, the future research directions are summarized, aiming at promoting the development of communication SEI.

Key words: communication specific emitter identification （SEI）, radio frequency fingerprint, few-shot, self-supervised learning, semi-supervised learning

中图分类号:

TN 911.7

杜鑫苹, 夏威. 小样本通信辐射源个体识别研究进展[J]. 系统工程与电子技术, 2026, 48(9): 3229-3240.

Xinping Du, Wei Xia. Research progress of few-shot communication specific emitter identification[J]. Systems Engineering and Electronics, 2026, 48(9): 3229-3240.

图/表 7

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表1

基于小样本学习的通信SEI最新研究总结"

方法	基础网络	训练范式	验证数据集	识别性能	优势	不足
DIR^[57]	ResNet	有监督	ADS-B^[11]	训练数据占比3%，Acc=81.6%	数据增强方法简单，易实现	数据增强方法多样性不足
PGAN^[35]	ResNet	有监督	私有NJUPT-RFF	7way 5shot，Acc=85%	基于GAN生成样本，质量高，多样性强	GAN结构复杂，训练不稳定，依赖高信噪比
SCSC^[55]	CNN	自监督	私有USRP-X310	10way 50shot, Acc=94.39%	可提取多层次RFFs特征	负样本对选择困难
GLD-CL^[53]	ResNet	无监督	ADS-B^[11]	NMI=92.86%	结合数据增强和组标签策略生成正样本对，聚类效果好	无法获取类别标签，负样本对选择困难
Improved BYOL^[60]	ResNet	自监督	私有数据集	8way 10shot, Acc=70.56%	无需构造负样本对，训练稳定	数据增强方法多样性不足
SA2SEI^[34]	CVCNN	自监督	WiFi^[13]	6way 5shot, Acc=83.40%	引入对抗增强方法，多样性强，无需构造负样本对，训练稳定	泛化性能不足
VNE^[62]	CVCNN	自监督	私有ADS-B	10way 10shot, Acc=65.07%	引入正则化策略，进一步提升泛化性能，无需构造负样本对，训练稳定	计算开销相对大
AMAE^[31]	CVCNN	自监督	WiFi^[13]	6way 10shot, Acc=92.68%	基于信号重建任务，泛化性能好	未考虑原始信号特征，需人为选择掩蔽比例
ADPMAE^[36]	CVCNN	自监督	WiFi^[13]	6way 10shot, Acc=95.75%	同时考虑原始信号和其掩蔽版本，结合自注意机制提升泛化性能，基于信号重建任务，泛化性能好	需人为选择掩蔽比例
MAT^[29]	CVCNN	半监督	WiFi^[13]，ADS-B^[11]	ratio=10%, Acc=84.80%，80.70%	结合伪标签和对抗性训练策略，泛化性能好	数据增强方法多样性不足，需人为选择置信度阈值
DCR^[28]	CVCNN	半监督	WiFi^[13]，ADS-B^[11]	ratio=10%, Acc=99.77%，90.10%	结合弱增强和强增强方法	在有标签样本充足时，识别性能下降，需人为选择置信度阈值
CTCR^[37]	CVCNN	半监督	ADS-B^[11]	ratio=10%, Acc=99.50%	可动态调整置信度阈值	对衰减因子等超参数仍需调优

表1

参考文献 83

1	Zhang Y B, Peng Y, Sun J L, et al. GPU-free specific emitter identification using signal feature embedded broad learning[J]. IEEE Internet of Things Journal, 2023, 10 (14): 13028. doi: 10.1109/JIOT.2023.3257479
2	He B X, Wang F G. Specific emitter identification via sparse Bayesian learning versus model-agnostic meta-learning[J]. IEEE Trans. on Information Forensics and Security, 2023, 18, 3677. doi: 10.1109/TIFS.2023.3287073
3	Zhang X L, Li T Y, Gong P, et al. Variable-modulation specific emitter identification with domain adaptation[J]. IEEE Trans. on Information Forensics and Security, 2022, 18, 380.
4	陈翔, 汪连栋, 许雄, 等. 基于RawI/Q和深度学习的射频指纹识别方法综述[J]. 雷达学报, 2022, 12 (1): 214.
5	Spezio A E. Electronic warfare systems[J]. IEEE Trans. on Microwave Theory and Techniques, 2002, 50 (3): 633. doi: 10.1109/22.989948
6	Xu Z W, Han G J, Liu L, et al. A lightweight specific emitter identification model for IIoT devices based on adaptive broad learning[J]. IEEE Trans. on Industrial Informatics, 2022, 19 (5): 7066. doi: 10.1109/tii.2022.3206309
7	Ureten O, Serinken N. Wireless security through RF fingerprinting[J]. Canadian Journal of Electrical and Computer Engineering, 2007, 32 (1): 27. doi: 10.1109/CJECE.2007.364330
8	金涛, 王晓峰, 田润澜, 等. 基于改进IDCNN+TCN的雷达辐射源快速识别方法[J]. 系统工程与电子技术, 2022, 44 (2): 463.
9	张立民, 谭凯文, 闫文君, 等. 基于多级跳线残差网络的雷达辐射源识别[J]. 系统工程与电子技术, 2022, 44 (7): 2148. doi: 10.12305/j.issn.1001-506X.2022.07.10
10	韦建宇, 俞璐. 通信SEI中的特征提取方法综述[J]. 通信技术, 2022, 55 (6): 681.
11	Tu Y, Lin Y, Zha H R, et al. Large-scale real-world radio signal recognition with deep learning[J]. Chinese Journal of Aeronautics, 2022, 35 (9): 35. doi: 10.1016/j.cja.2021.08.016
12	Qian Y H, Qi J, Kuai X Y, et al. Specific emitter identification based on multi-level sparse representation in automatic identification system[J]. IEEE Trans. on Information Forensics and Security, 2021, 16, 2872. doi: 10.1109/TIFS.2021.3068010
13	Sankhe K, Belgiovine M, Zhou F, et al. ORACLE: optimized radio classification through convolutional neural networks[C]// IEEE Conference on Computer Vision and Pattern Recognition, 2019: 370.
14	Shen G X, Zhang J Q, Marshall A, et al. Towards scalable and channel-robust radio frequency fingerprint identification for LoRa[J]. IEEE Trans. on Information Forensics and Security, 2022, 17, 774. doi: 10.1109/TIFS.2022.3152404
15	Xie L N, Peng L N, Zhang J Q, et al. Radio frequency fingerprint identification for internet of things: a survey[J]. Security and Safety, 2024, 3, 2023022. doi: 10.1051/sands/2023022
16	Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521 (7553): 436. doi: 10.1038/nature14539
17	Yu J B, Hu A Q, Li G Y, et al. A robust RF fingerprinting approach using multisampling convolutional neural network[J]. IEEE Internet of Things Journal, 2019, 6 (4): 6786. doi: 10.1109/JIOT.2019.2911347
18	Merchant K, Revay S, Stantchev G, et al. Deep learning for RF device fingerprinting in cognitive communication networks[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12 (1): 160. doi: 10.1109/JSTSP.2018.2796446
19	Ding L D, Wang S L, Wang F G, et al. Specific emitter identification via convolutional neural networks[J]. IEEE Communications Letters, 2018, 22 (12): 2591. doi: 10.1109/LCOMM.2018.2871465
20	曲凌志, 杨俊安, 刘辉, 等. 嵌入注意力机制的通信 SEI 方法[J]. 系统工程与电子技术, 2022, 44 (1): 20.
21	王春升, 王永民, 许华, 等. 基于残差原型网络的 SEI[J]. 系统工程与电子技术, 2023, 45 (7): 2249.
22	Wang Y, Gui G, Gacanin H, et al. An efficient specific emitter identification method based on complex-valued neural networks and network compression[J]. IEEE Journal on Selected Areas in Communications, 2021, 39 (8): 2305. doi: 10.1109/JSAC.2021.3087243
23	Tang P, Ding G R, Xu Y T, et al. Causal learning for robust specific emitter identification over unknown channel statistics[J]. IEEE Trans. on Information Forensics and Security, 2024, 19, 5316. doi: 10.1109/TIFS.2024.3393237
24	Huang S N, Guo L T, Fu X, et al. Open-set specific emitter identification leveraging enhanced metric denoising autoencoders[J]. IEEE Internet of Things Journal, 2024, 12 (4): 3453. doi: 10.1109/jiot.2024.3404042
25	Zhou Z N, Li G Y, Wang T, et al. A robust open-set specific emitter identification for complex signals with class-irrelevant features[J]. IEEE Trans. on Information Forensics and Security, 2025, 20, 6058. doi: 10.1109/TIFS.2025.3576573
26	Wang J Z, Wang H H, Wang W, et al. OS-SEI: open-set specific emitter identification based on outlier exposure and label smoothing[J]. IEEE Internet of Things Journal, 2025, 12 (21): 44128. doi: 10.1109/jiot.2025.3560233
27	Jian T, Rendon B C, Ojuba E, et al. Deep learning for RF fingerprinting: a massive experimental study[J]. IEEE Internet of Things Magazine, 2020, 3 (1): 50. doi: 10.1109/IOTM.0001.1900065
28	Fu X, Shi S N, Wang Y, et al. Semi-supervised specific emitter identification via dual consistency regularization[J]. IEEE Internet of Things Journal, 2023, 10 (21): 19257. doi: 10.1109/JIOT.2023.3281668
29	Fu X, Peng Y, Liu Y C, et al. Semi-supervised specific emitter identification method using metric-adversarial training[J]. IEEE Internet of Things Journal, 2023, 10 (12): 10778. doi: 10.1109/JIOT.2023.3240242
30	桂冠, 陶梦圆, 王诚, 等. 面向特定辐射源识别的小样本学习方法综述[J]. 南通大学学报（自然科学版）, 2023, 22 (3): 1. doi: 10.12194/j.ntu.20220928001
31	Yao Z S, Fu X, Guo L T, et al. Few-shot specific emitter identification using asymmetric masked auto-encoder[J]. IEEE Communications Letters, 2023, 27 (10): 2657. doi: 10.1109/LCOMM.2023.3312669
32	Wang C, Fu X, Wang Y, et al. Few-shot specific emitter identification via hybrid data augmentation and deep metric learning[EB/OL]. [2025-08-04]. https: //arxiv.org/abs/2212.00252.
33	Wang Y, Gui G, Lin Y, et al. Few-shot specific emitter identification via deep metric ensemble learning[J]. IEEE Internet of Things Journal, 2022, 9 (24): 24980. doi: 10.1109/JIOT.2022.3194967
34	Liu C, Fu X, Wang Y, et al. Overcoming data limitations: a few-shot specific emitter identification method using self-supervised learning and adversarial augmentation[J]. IEEE Trans. on Information Forensics and Security, 2024, 19, 500. doi: 10.1109/TIFS.2023.3324394
35	Han G J, Wang W T, Xu Z W, et al. Data augmentation and individual identification method for emitters using contour stella image mapping[J]. IEEE Trans. on Cognitive Communications and Networking, 2024, 10 (4): 1253. doi: 10.1109/TCCN.2024.3378214
36	Yang S, Yu S, Li Q, et al. Few-shot specific emitter identification via asymmetric dual-path masked autoencoder[J]. Digital Signal Processing, 2025, 163, 105201. doi: 10.1016/j.dsp.2025.105201
37	Xu Q Y, Liu L X, Zhou X. Boosting semi-supervised specific emitter identification with curriculum triple consistency regularization[C]//International Joint Conference on Neural Networks, 2024.
38	Zhang X X, Wang Y, Zhang Y B, et al. Data augmentation aided few-shot learning for specific emitter identification[C]//IEEE 96th Vehicular Technology Conference, 2022.
39	Cai Z R, Ma W X, Wang X R, et al. The performance analysis of time series data augmentation technology for small sample communication device recognition[J]. IEEE Trans. on Reliability, 2022, 72 (2): 574. doi: 10.1109/tr.2022.3178707
40	Fu X, Wang Y, Lin Y, et al. A novel semi-supervised learning framework for specific emitter identification[C]//IEEE 96th Vehicular Technology Conference, 2022.
41	俞佳宝, 胡爱群, 朱长明, 等. 无线通信设备的射频指纹提取与识别方法[J]. 密码学报, 2016, 3 (5): 433.
42	Fadul M K, Reising D R, Weerasena L P, et al. Improving RF-DNA fingerprinting performance in an indoor multipath environment using semi-supervised learning[J]. IEEE Trans. on Information Forensics and Security, 2024, 19, 3194. doi: 10.1109/TIFS.2024.3360851
43	He B X, Wang F G. Cooperative specific emitter identification via multiple distorted receivers[J]. IEEE Trans. on Information Forensics and Security, 2020, 15, 3791. doi: 10.1109/TIFS.2020.3001721
44	李天琪, 张玉, 张进, 等. 基于时频与快速熵的 IFF 辐射源个体识别方法[J]. 探测与控制学报, 2020, 42 (1): 87.
45	刘家豪, 郭英, 孟涛, 等. 基于 EMD 和 SVD 特征提取的通信辐射源个体识别方法[J]. 空军工程大学学报（自然科学版）, 2021, 22 (4): 63. doi: 10.3969/j.issn.1009-3516.2021.04.010
46	Liao Y L, Li H Z, Cao Y Z, et al. Fast Fourier transform with multi-head attention for specific emitter identification[J]. IEEE Trans. on Instrumentation and Measurement, 2024, 73, 2503812.
47	Xu L, Fu X, Wang Y, et al. Enhanced few-shot specific emitter identification via phase shift prediction and decoupling[J]. IEEE Trans. on Cognitive Communications and Networking, 2024, 11 (1): 145. doi: 10.1109/tccn.2024.3435886
48	Nielsen M A. Neural networks and deep learning[M]. San Francisco: Determination Press, 2015.
49	Goodfellow I, Bengio Y, Courville A, et al. Deep learning[M]. Cambridge: MIT Press, 2016.
50	Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems, 2012.
51	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770.
52	Hanna S, Karunaratne S, Cabric D. Wisig: a large-scale WiFi signal dataset for receiver and channel agnostic RF fingerprinting[J]. IEEE Access, 2022, 10, 22808. doi: 10.1109/ACCESS.2022.3154790
53	Yang N, Zhang B N, Guo D X. Unsupervised specific emitter identification via group label-driven contrastive learning[J]. Electronics, 2025, 14 (11): 2136. doi: 10.3390/electronics14112136
54	Zhao D X, Yang J A, Liu H, et al. Specific emitter identification model based on improved BYOL self-supervised learning[J]. Electronics, 2022, 11 (21): 3485. doi: 10.3390/electronics11213485
55	Hao X Y, Feng Z X, Liu R Y, et al. Contrastive self-supervised clustering for specific emitter identification[J]. IEEE Internet of Things Journal, 2023, 10 (23): 20803. doi: 10.1109/JIOT.2023.3284428
56	Miyato T, Maeda S, Koyama M, et al. Virtual adversarial training: a regularization method for supervised and semi-supervised learning[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2018, 41 (8): 1979. doi: 10.1109/tpami.2018.2858821
57	Peng Y, Zhang X L, Guo L T, et al. Enhanced specific emitter identification with limited data through dual implicit regularization[J]. IEEE Internet of Things Journal, 2024, 11 (15): 26395. doi: 10.1109/JIOT.2024.3395441
58	Chen T, Kornblith S, Norouzi M, et al. A simple framework for contrastive learning of visual representations[C]//International Conference on Machine Learning, 2020: 1597.
59	Lin T Y, Dollar P, Girshick R, et al. Feature pyramid networks for object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2117.
60	Grill J B, Strub F, Altchf E, et al. Bootstrap your own latent——a new approach to self-supervised learning[C]//Advances in Neural Information Processing Systems, 2020.
61	Liu X, Zhang F J, Hou Z Y, et al. Self-supervised learning: generative or contrastive[J]. IEEE Trans. on Knowledge and Data Engineering, 2021, 35 (1): 857.
62	Zhang J B, Liu Q S, An S Q, et al. Cross-domain few-shot specific emitter identification via contrastive self-supervised learning[J]. IEEE Communications Letters, 2025, 29 (7): 1564. doi: 10.1109/LCOMM.2025.3568388
63	He K M, Chen X L, Xie S N, et al. Masked autoencoders are scalable vision learners[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 16000.
64	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems, 2017.
65	Fan Y, Kukleva A, Dai D X, et al. Revisiting consistency regularization for semi-supervised learning[J]. International Journal of Computer Vision, 2023, 131 (3): 626. doi: 10.1007/s11263-022-01723-4
66	Lee D H. Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks[C]//Workshop on Challenges in Representation Learning, 2013.
67	Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning[C]//Advances in Neural Information Processing Systems, 2017.
68	Sagi O, Rokach L. Ensemble learning: a survey[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018, 8 (4): e1249. doi: 10.1002/widm.1249
69	Wang X, Chen Y D, Zhu W W. A survey on curriculum learning[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2021, 44 (9): 4555.
70	Yan G L, Fu X, Wang Y, et al. Robust few-shot specific emitter identification using multi-view feature fusion with attention[C]//IEEE 101st Vehicular Technology Conference, 2025.
71	Fadul M K, Reising D R, Weerasena L P. An investigation into the impacts of deep learning-based re-sampling on specific emitter identification performance[J]. Journal of Engineering, 2023, 2023 (11): e12327. doi: 10.1049/tje2.12327
72	Zhang Y B, Zhang Q Y, Zhao H T, et al. Multisource heterogeneous specific emitter identification using attention mechanism-based RFF fusion method[J]. IEEE Trans. on Information Forensics and Security, 2024, 19, 2639. doi: 10.1109/TIFS.2024.3353594
73	Kulhandjian H, Batz E, Garcia E, et al. AI-based RF-fingerprinting framework and implementation using software-defined radios[C]//International Conference on Computing, Networking and Communications, 2023: 143.
74	Wang C S, Wang Y M, Zhang Y, et al. Open-set specific emitter identification based on prototypical networks and extreme value theory[J]. Applied Sciences, 2023, 13 (6): 3878. doi: 10.3390/app13063878
75	Deng P F, Hong S H, Qi J, et al. A lightweight transformer-based approach of specific emitter identification for the automatic identification system[J]. IEEE Trans. on Information Forensics and Security, 2023, 18, 2303. doi: 10.1109/TIFS.2023.3266627
76	Gan J Y, Hua K, Kang Z Y, et al. SAS-SEINet: a SNR-aware adaptive scalable SEI neural network accelerator using algorithm-hardware co-design for high-accuracy and power-efficient UAV surveillance[J]. Sensors, 2022, 22 (17): 6532. doi: 10.3390/s22176532
77	Al-Sa'd M F, Al-Ali A, Mohamed A, et al. RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database[J]. Future Generation Computer Systems, 2019, 100, 86. doi: 10.1016/j.future.2019.05.007
78	Gan J Y, Li Q, Shao H Z, et al. A zynq-based platform with conditional-reconfigurable complex-valued neural network for specific emitter identification[J]. IEEE Trans. on Instrumentation and Measurement, 2024, 73, 5502711. doi: 10.1109/tim.2024.3391818
79	李奇真, 刘佳旭, 梁先明, 等. 基于深度学习的跨域辐射源个体识别综述[J]. 电讯技术, 2024, 64 (7): 1163. doi: 10.20079/j.issn.1001-893x.231030001
80	Ben-David S, Blitzer J, Crammer K, et al. Analysis of representations for domain adaptation[C]//Advances in Neural Information Processing Systems, 2006.
81	Wan H, Wang Q, Fu X, et al. VC-SEI: robust variable-channel specific emitter identification method using semi-supervised domain adaptation[J]. IEEE Trans. on Wireless Communications, 2024, 23 (12): 18228. doi: 10.1109/TWC.2024.3463740
82	Li D Z, Chen X W, Hong S H, et al. FSCIL-SEI: few-shot class-incremental learning approach for specific emitter identification[J]. IEEE Trans. on Instrumentation and Measurement, 2025, 74, 2504914.
83	Wu Y, Chen Y P, Wang L J, et al. Large scale incremental learning[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 374.

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[13]	李旭, 丁萌, 魏东辉, 吴晓舟, 曹云峰. VDAS中基于单目红外图像的深度估计方法[J]. 系统工程与电子技术, 2021, 43(5): 1210-1217.
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