A Method for Specific Communication Emitter Identification Based on Multi-Domain Feature Fusion

doi:10.12382/bgxb.2021.0880

Abstract

Abstract:

To solve the problem of low identification rate caused by specific communication emitter identification using a single feature, a method for individual identification of the communication emitter based on multi-domain feature fusion is proposed. Firstly, multiple transform domain features of the signals transmitted by the communication emitter are extracted, and these features are combined into multi-domain features.Secondly, the multi-channel convolution neural network is constructed, and the multi-channel convolution operation is carried out to extract the multi-domain features at a deep level. Finally, specificcommunication emitter classification is completed using the neural network classifier.Compared with the identification method based on a single feature, this method makes full use of the multi-domain features of the signals sent by the communication emitter and combines the powerful microscopic feature mining capability of the neural network to realize the effective individual identification of the communication emitter. Through the identification of 20 CC2530 devices under the conditions of low signal-to-noise ratio(SNR) and Rayleigh channel, the results show that the proposed method can significantly improve the identification accuracy and timeliness under a low SNR, and that the identification effect can still reach 91.01% under the condition of 0dB.

Key words: communication emitter identification, feature fusion, multi-channel convolution neural network

WANG Jian, ZHANG Bangning, ZHANG Jie, WEI Guofeng, GUO Daoxing. A Method for Specific Communication Emitter Identification Based on Multi-Domain Feature Fusion[J]. Acta Armamentarii, 2023, 44(4): 949-959.

Figures/Tables 14

Fig.1 Specific communication emitter identification model based on multi-domain features

Fig.2 Emitter and data acquisition system

Fig.3 Standardized preamble I/Q data

Fig.4 Segmentation method of the Welch algorithm

Fig.5 Power spectrum and spectrum of preamble with SNR of 0dB

Fig.6 Bispectrum contour map of preamble

Fig.7 Rectangle integration path

Fig.8 Schematic diagram of feature fusion data

Fig.9 Model of multi-channel one-dimensional CNN structure

Fig.10 I/Q data in Rayleigh Channel

Table 1 Identification results of different training epochs on 0dB signal%

实验方法	epoch
实验方法	4	8	12	16	20
多域特征融合	87.08	89.10	89.92	90.45	91.01
基于I/Q数据	66.74	75.01	80.78	84.01	85.17
基于功率谱数据	71.76	79.70	84.15	86.63	87.55
基于积分双谱数据	61.50	68.89	72.02	74.11	76.50

Fig.11 Comparison of identification effect between single transform domain and multi-domain feature fusion

Table 2 Identification results of single transform domain and multi-domain feature fusion at 20 epochs%

实验方法	信噪比/dB
实验方法	0	5	10	15	20	25
多域特征融合	91.01	94.83	96.58	98.15	98.48	98.98
基于I/Q数据	85.17	89.23	90.75	92.11	93.25	93.88
基于功率谱数据	87.55	91.93	93.25	93.35	93.60	94.00
基于积分双谱数据	76.50	83.30	88.50	91.15	92.55	93.05

Fig.12 Comparison of identification effects of different algorithms

References 33

[1]	XU Q, ZHENG R, SAAD W, et al. Device fingerprinting in wireless networks:challenges and opportunities[J]. IEEE Communications Surveys & Tutorials, 2016, 18(1):94-104.
[2]	WANG S, PENG L N, FU H, et al. Aconvolutional neural network-based RF fingerprinting identification scheme for mobile phones[C]// Proceedings of IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops.Toronto, ON, Canada: IEEE Press, 2020:15-120.
[3]	PENG L N, ZHANG J Q, LIU M, et al. Deep learning based RF fingerprint identification using differential constellation trace figure[J]. IEEE Transactions on Vehicular Technology, 2019, 69(1):1091-1095. doi: 10.1109/TVT.25 URL
[4]	TAMURA H, ANAGISAWA K, SHIRANE A, et al. Wireless devices identification with light-weight convolutional neural network operating on quadrant IQ transition image [C]//Proceedings of the 2020 18th IEEE International New Circuits and Systems Conference. Montreal, QC, Canada: IEEE Press, 2020:106-109.
[5]	RU X H, LIU Z, HUANG Z T, et al. Evaluation of unintentional modulation for pulse compress-ion signals based on spectrum asymmetry[J]. IET Radar, Sonar & Navigation, 2017, 11(4):656-663. doi: 10.1049/rsn2.v11.4 URL
[6]	WANG W, LIU H, YANG J A, et al. Specific emitter identification using decomposed hierarchical feature extraction methods[C]// Proceedings of International Conference on Natural Computation.Piscataway, NJ, US: IEEE Press, 2017:1639-1643.
[7]	WONG L J, HEADLEY W C, MICHAELS A J. Specific emitter identification using convolutional neural network-based IQ imbalance estimators[J]. IEEE Access, 2019, 7:33544-33555. doi: 10.1109/Access.6287639 URL
[8]	RAM M, BANER C, ROY D, et al. Exploitingspatio-temporal properties of I/Q signal data using 3D convolution for RF transmitter ientification[J]. IEEE Journal of Radio Frequency Identification, 2021, 99:113-127.
[9]	LING Y, JIA J C, WANG S, et al. Wireless device identification based on radio frequency fingerprint features[C]//Proceedings of the 2020 IEEE International Conference on Communications. Dublin, Ireland:IEEE, 2020:1-6.
[10]	COGHILL C, REHMAN S U, SOWERBY K W. Radio-frequency fingerprinting for mitigating primary user emulation attack in low-end cognitive radios[J]. IET Communications, 2014, 8(8):1274-128 doi: 10.1049/cmu2.v8.8 URL
[11]	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):2591-2594. doi: 10.1109/LCOMM.2018.2871465 URL
[12]	YAO Y Y, YU L, CHEN Y M. Specific emitter identification based on square integral bispectrum features[C]//Proceedings of the 2020 IEEE 20th International Conference on Communication Technology. Nanning, China:IEEE, 2020:1311-1314.
[13]	GONG J L, XU X D, LEI Y K. Unsupervised specific emitter identification method using radio-frequency fingerprint embedded infoGAN[J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 2898-2913. doi: 10.1109/TIFS.10206 URL
[14]	CHEN S L, XIE F Y, CHEN Y, et al. Identification of wireless transceiver devices using radio frequency (RF) fingerprinting based on STFT analysis to enhance authentication security[C]//Proceedings of the 2017 IEEE 5th International Symposium on Electromagnetic Compatibility. Beijing, China: IEEE, 2017:1-5.
[15]	TU Y, ZHANG Z, LI Y B, et al. Research on the Internet of Things device recognition based on RF-fingerprinting[J]. IEEE Access, 2019, 7:37426-37431. doi: 10.1109/Access.6287639 URL
[16]	ZHOU X Y, HU A Q, LI G Y, et al. A robust radio-frequency fingerprint extraction scheme for practical device recognition[J]. IEEE Internet of Things Journal, 2021, 99: 1276-11289.
[17]	Wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (WPANs):IEEE 802.15.4[S]. New York, NY, US:IEEE, 2006.
[18]	MOSTAFA A E, WONG V W, ZHOU Y, et al. Aggregate preamble sequence design and detection for massive IoT with deep learning[J]. IEEE Transactions on Vehicular Technology, 2021, 70(4):3800-3816. doi: 10.1109/TVT.2021.3064868 URL
[19]	RAMSEY B W, TEMPLE M A, MULLINS B E. PHY foundation for multi-factor ZigBee node authentication[C]//Proceedings of the 2012 IEEE Global Communications Conference. Anaheim, CA, US: IEEE Press, 2012:795-800.
[20]	Al-SHAWABKA A, RESTUCCIA F, D’ORO S, et al. Massive-scale I/Q datasets for WiFi radio fingerprinting[J]. Computer Networks, 2020, 182: 107566. doi: 10.1016/j.comnet.2020.107566 URL
[21]	MITRA S K. Digital signal processing:a computer-based approach[M]. 3rd ed. Tsinghua University Press, 2012.
[22]	PARHI K, AYINAL M. Low-complexity welch power spectral density computation[J]. IEEE Transactions on Circuits and Systems I:Regular Papers, 2014, 61(1):172-182. doi: 10.1109/TCSI.2013.2264711 URL
[23]	ZHANG G C, DU R X, SHI T L. Extracting gear fault features using integrated bispectrum[C]//Robotics Intelligent Systems and Signal Processing. Changsha, China: IEEE, 2003:548-553.
[24]	WEI D X, ZHANG S N, CHEN S, et al. Research on anti-jamming technology of chaotic composite shortrange detection system based on underdetermined signal separation and spectral analysis[J]. IEEE Access, 2019, 7:42298-42308. doi: 10.1109/Access.6287639 URL
[25]	FAILLI M. Digital land mobile radio communications:COST 207[R]. London: Commission of the European Communities, 1989.
[26]	FADUL M, REISING D, LOVELESS T, et al. RF-DNA fingerprint classification of OFDM signals using a rayleigh fading channel model[C]// Proceedings of the 2019 IEEE Wireless Communications and Networking Conference. Marrakesh, Morocco: IEEE Press, 2019:1-7.
[27]	李晨明. 非合作通信中的盲均衡技术研究[D]. 西安: 西安电子科技大学, 2020:111.
	LI C. Research on Equalization Technology in Non-cooperative Communication[D]. Xi’an: Xidian University, 2020:111. (in Chinese)
[28]	Al-SHAWABKA A, RESTUCCIA F, D’ORO S, et al. Exposing the fingerprint: dissecting the impact of the wireless channel on radio fingerprinting[C]//Proceedings of the IEEE INFOCOM 2020-IEEE Conference on Computer Communications. Toronto, Canada: IEEE, 2020:646-655.
[29]	石荣, 吴聪. 信噪比的不同定义及对调制识别性能评估的影响[J]. 通信技术, 2019, 52(7):1556-1562.
	SHI R, WU C. Different definitions of SNR and its influence on performance evaluation of modulation recognition[J]. Communications Technology, 2019, 52(7):1556-1562. (in Chinese)
[30]	PAN Y W, YANG S H, PENG H, et al. Specific emitter identification based on deep residual networks[J]. IEEE Access, 2019, 7:54425-54434. doi: 10.1109/ACCESS.2019.2913759
[31]	陈悦, 雷迎科, 李昕, 等. 基于IQ图特征的通信辐射源个体识别[J]. 信号处理, 2021, 37(1):120-125.
	CHEN Y, LEI Y K, LI X, et al. Specific emitter identification of communication radiation source based on the characteristics IQ graph features[J]. Journal of Signal Processing, 2021, 37(1):120-125. (in Chinese)
[32]	梁华东, 韩江洪. 采用双谱多类小波包特征的雷达信号聚类分选[J]. 光子学报, 2014, 43(3):158-165.
	LIANG H D, HAN J H. Clustering and sorting radar signals based on multi wavelet packets characteristics of bispectrum[J]. Acta Photonica Sinica, 2014, 43(3):158-165. (in Chinese)
[33]	ZHAO W Z, DU S H. Spectral-spatial feature extraction for hyperspectral image classifi-cation: a dimension reduction and deep learning approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8):4544-4554. doi: 10.1109/TGRS.2016.2543748 URL