基于信号递归图和卷积宽度学习的小样本辐射源个体识别方法

doi:10.12305/j.issn.1001-506X.2025.09.30

Abstract

Abstract:

In order to solve the problem that the current specific emitter identification methods are easy to overfit and have low recognition accuracy under the condition of small samples, a few-shot specific emitter identification method based on signal recurrence plot and convolutional broad learning is proposed. In this method, the emitter signal is converted into a recurrence plot as the input of the broad learning network, and the time series features of the emitter data are transformed into image spatial features. In addition, a convolutional broad learning network is proposed, which replaces the computation method of feature nodes in broad learning from matrix multiplication to convolution operation, and reduces the number of model parameters through sparse joining and weight sharing, thereby reducing the risk of model overfitting. Through experiments on public datasets, it is verified that the proposed algorithm has better recognition performance than other algorithms under the condition of a small number of training samples.

Key words: recurrence plot, convolutional broad learning, few-shot, specific emitter identification

CLC Number:

Yupeng CHEN, Keju HUANG, Hui LIU, Longkun KUANG, Junan YANG. Few-shot specific emitter identification method based on signal recurrence plot and convolutional broad learning[J]. Systems Engineering and Electronics, 2025, 47(9): 3086-3092.

Figures/Tables 9

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References 30

1	胡苏, 马上, 林迪, 等. 基于射频指纹技术的卫星信号个体识别方法[J]. 信息对抗技术, 2022, 1 (1): 55- 61. doi: 10.12399/j.issn.2097-163x.2022.01.005
	HU S, MA S, LIN D, et al. Identification of individual satellite signal based on radio frequency fingerprint technology[J]. Information Countermeasure Technology, 2022, 1 (1): 55- 61. doi: 10.12399/j.issn.2097-163x.2022.01.005
2	ZENG M Y, LIU Z G, WANG Z W. An adaptive specific emitter identification system for dynamic noise domain[J]. IEEE Internet of Things Journal, 2022, 9 (24): 25117- 25135. doi: 10.1109/JIOT.2022.3195450
3	王春升, 王永民, 许华, 等. 基于残差原型网络的辐射源个体识别[J]. 系统工程与电子技术, 2023, 45 (7): 2249- 2258.
	WANG C S, WANG Y M, XU H, et al. Specific emitter identification based on residual prototype network[J]. Systems Engineering and Electronics, 2023, 45 (7): 2249- 2258.
4	MERCHANT K, REVAY S, STANTCHEV G, et al. Deep learning for RF device fingerprinting in cognitive communication networks[J]. IEEE Journal on Selected Topics in Signal Processing, 2018, 12 (1): 160- 167. doi: 10.1109/JSTSP.2018.2796446
5	SABINE A, ALEXANDER C, GERD A. Identification of radar emitter type with recurrent neural networks[C]// Proc. of the Sensor Signal Processing for Defence Conference, 2020.
6	ZHANG Y Z, ZHOU Z N, LI X P. Generative adversarial networks with gramian angular field for handling imbalanced data in specific emitter identification[J]. Signal, Image and Video Processing, 2024, 18 (3): 2929- 2938. doi: 10.1007/s11760-023-02960-x
7	YAN G L, CAI Z X, LIU Y C. Intelligent specific emitter identification using complex-valued convolutional neural network[C]// Proc. of the IEEE 23rd International Conference on Communication Technology, 2023: 1259−1263.
8	曲凌志, 杨俊安, 刘辉, 等. 嵌入注意力机制的通信辐射源个体识别方法[J]. 系统工程与电子技术, 2022, 44 (1): 20- 27. doi: 10.12305/j.issn.1001-506X.2022.01.03
	QU L Z, YANG J A, LIU H, et al. Method for individual identific-ation of communication radiation source embedded in attention mechanism[J]. Systems Engineering and Electronics, 2022, 44 (1): 20- 27. doi: 10.12305/j.issn.1001-506X.2022.01.03
9	LIU Y H, XU H, QI Z S. Specific emitter identification against unreliable features interference based on time-series classification network structure[J]. IEEE Access, 2020, 8, 200194- 200208. doi: 10.1109/ACCESS.2020.3035813
10	GUO L T, LIU C B, LIU Y C. Towards open-set specific emitter identific-ation using auxiliary classifier generative adversarial network and OpenMax[J]. IEEE Trans. on Cognitive Communications and Networking, 2024, 10(6): 2019−2028.
11	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- 16. doi: 10.12194/j.ntu.20220928001
	GUI G, TAO M Y, WANG C, et al. Survey of few-shot learning methods for specific emitter identification[J]. Journal of Nantong University（Natural Science Edition）, 2023, 22 (3): 1- 16. doi: 10.12194/j.ntu.20220928001
12	ZHANG W J, SHI F, ZHANG Q Y, et al. Few-shot specific emitter identification leveraging neural architecture search and advanced deep transfer learning[J]. IEEE Internet of Things Journal, 2024, 11 (8): 30087- 30093.
13	LIU M Q, CHAI Y E, LI M. Transfer learning-based specific emitter identification for ADS-B over satellite system[J]. Remote Sensing, 2024, 16 (12): 2068. doi: 10.3390/rs16122068
14	YANG N, ZHANG B N, DING G R, et al. Specific emitter identification wi-th limited samples: a model-agnostic meta-learning approach[J]. IEEE Communications Letters, 2022, 26 (2): 345- 349. doi: 10.1109/LCOMM.2021.3110775
15	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- 3691. doi: 10.1109/TIFS.2023.3287073
16	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- 1262.
17	ZHANG X X, WANG Y, ZHANG Y B. Data augmentation aided few-shot learning for specific emitter identification[C]//Proc. of the IEEE 96th Vehicular Technology Conference, 2022.
18	WANG C, FU X, WANG Y, et al. Interpolative metric learning for few-shot specific emitter identification[J]. IEEE Trans. on Vehicular Technology, 2023, 72 (12): 16851- 16855. doi: 10.1109/TVT.2023.3326765
19	WANG Y, GUI G, LIN Y. Few-shot specific emitter identification via deep metric ensemble learning[J]. IEEE Internet of Things Journal, 2022, 9 (24): 24980- 24994. doi: 10.1109/JIOT.2022.3194967
20	WU Z T, WANG F G, HE B X. Specific emitter identification via contrastive learning[J]. IEEE Communications Letters, 2023, 27 (4): 1160- 1164. doi: 10.1109/LCOMM.2023.3264431
21	LIU B, YU H Y, DU J P. Specific emitter identification based on self-supervised contrast learning[J]. Electronics, 2022, 11 (18): 2907.
22	CHEN C L, LIU Z L. Broad learning system: an effective and efficient incremental learning system without the need for deep architecture[J]. IEEE Trans. on Neural Networks and Learning Systems, 2018, 29 (1): 10- 24. doi: 10.1109/TNNLS.2017.2716952
23	CHEN C L, LIU Z L. Broad learning system: a new learning paradigm and system without going deep[C]// Proc. of the 32nd Youth Academic Annual Conference of Chinese Association of Automation, 2017: 1271−1276.
24	ZHANG Y, GONG B, WANG Q. BLS-identification: a device fingerprint classification mechanism based on broad learning for internet of things[J]. Digital Communications and Networks, 2024, 10 (3): 728- 739.
25	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, 2023, 19 (5): 7066- 7075. doi: 10.1109/TII.2022.3206309
26	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- 13039. doi: 10.1109/JIOT.2023.3257479
27	ZHOU Z Y, WEN J Q, LIU M X, et al. Classifying aquatic organism via evolving residual convolutional neural networks by optimized random vector fu-nctional link[J]. Multimedia Tools and Applications, 2024, 84, 221- 243.
28	陈梅辉. 基于递归图的多元生理时间序列复杂网络研究[D]. 徐州: 中国矿业大学, 2023.
	CHEN M H. Study on complex network of multivariate physiological time series based on recurrence plots[D]. Xuzhou: China University of Mining and Technology, 2023.
29	SAMER H, SAMURDHI K, DANIJELA C. WiSig: a large-Scale WiFi signal dataset for receiver and channel agnostic RF fingerprinting[J]. IEEE Access, 2022, 10, 22808- 22818. doi: 10.1109/ACCESS.2022.3154790
30	姚步泉. 智能通信辐射源个体识别研究[D]. 长沙: 国防科技大学, 2020.
	YAO B Q. Specific emitter identification via artificial intelligence[D]. Changsha: National University of Defense Technology, 2020.

参数	参数值
特征节点窗口个数$o$	8
特征节点个数$n$	8
增强节点个数$m$	3000
正则化系数$\lambda $	10⁻⁶

方法	优化方法	学习率	丢弃率	训练轮数	训练批大小	测试批大小
FCN	Adam	0.0001	0.1	50	100	250
RESNET	Adam	0.0001	0.1	50	100	250
STC-CVCNN	Adam	0.001	0.5	200	64	64

参数	BLN	RP-BLN	FCN	RESNET	SFEBLN	CBLN	RP-CBLN	STC-CVCNN
训练时间	0.59	24.13	2.92	9.61	0.66	0.82	67.10	568.35
识别时间	0.05	0.45	0.33	0.38	0.06	0.18	66.74	1.78

[1]	Wenqiang SHI, Yingke LEI, Hu JIN, Fei TENG. Algorithm for specific emitter identification based on adaptive wavelet decomposition and lightweight network architecture [J]. Systems Engineering and Electronics, 2025, 47(9): 2785-2796.
[2]	Chunsheng WANG, Yongmin WANG, Hua XU, Huali ZHU. Specific emitter identification based on residual prototype network [J]. Systems Engineering and Electronics, 2023, 45(7): 2249-2258.
[3]	Dong WANG, Zhongma CUI, Wendong CHEN, Qin SHU. Local projective noise reduction algorithm based on fuzzy recurrence and optimal hard threshold [J]. Systems Engineering and Electronics, 2023, 45(3): 621-628.
[4]	Juncheng GUO, Gang WAN, Xinjie HU, Fabao YAN, Shuai WANG. Meta-learning method for solar radio spectrum burst recognition [J]. Systems Engineering and Electronics, 2022, 44(8): 2410-2418.
[5]	Bowei QIN, Lei JIANG, Hua XU, Zisen QI. Modulation recognition algorithm based on residual generation adversarial network [J]. Systems Engineering and Electronics, 2022, 44(6): 2019-2026.
[6]	Qi LIU, Xinyu ZHANG, Yongxiang LIU. Few-shot SAR target recognition based on gated multi-scale matching network [J]. Systems Engineering and Electronics, 2022, 44(11): 3346-3356.
[7]	Yunhao SHI, Hua XU, Wanze ZHENG, Yinghui LIU. Few-shot modulation recognition method based on ensemble learning and feature dimension reduction [J]. Systems Engineering and Electronics, 2021, 43(4): 1099-1109.
[8]	Cunxiang XIE, Limin ZHANG, Zhaogen ZHONG. Specific emitter identification based on Hilbert-Huang transform and adversarial training [J]. Systems Engineering and Electronics, 2021, 43(12): 3478-3487.
[9]	Chongyu PAN, Jian HUANG, Jianguo HAO, Jianxing GONG, Zhongjie ZHANG. Survey of weakly supervised learning integrating zero-shot and few-shot learning [J]. Systems Engineering and Electronics, 2020, 42(10): 2246-2256.
[10]	ZHOU Zhiwen, HUANG Gaoming, WANG Xuebao, MAN Xin. Specific emitter identification based on joint collaborative represenation [J]. Systems Engineering and Electronics, 2019, 41(4): 724-729.
[11]	LIU Mingqian, YAN Zhiwen, ZHANG Junlin. Specific emitter identification method for aerial target [J]. Systems Engineering and Electronics, 2019, 41(11): 2408-2415.
[12]	WANG Guiliang, HUANG Yuanling. Analysis of the influence of Doppler and Doppler rateofchange on the specific emitter identification#br# [J]. Systems Engineering and Electronics, 2017, 39(12): 2671-2676.
[13]	HAN Tao, ZHOU Yi-yu . Intuitive systemic models for radar specific emitter identification [J]. Journal of Systems Engineering and Electronics, 2013, 35(4): 735-739.
[14]	XU Dan, ZHANG Heng-yang, JIANG Wen-li, ZHOU Yi-yu. Specific emitter identification based on frequency domain robust ε-contaminated method [J]. Journal of Systems Engineering and Electronics, 2009, 31(1): 57-61.
[15]	XU Dan, ZHANG Heng-yang, JIANG Wen-li, ZHOU Yi-yu. Specific emitter identification based on frequency domain robust ε-contaminated method [J]. Journal of Systems Engineering and Electronics, 2009, 31(01): 57-61.

Few-shot specific emitter identification method based on signal recurrence plot and convolutional broad learning

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