| Citation: | Yiwei PAN, Sihan YANG, Hua PENG, Tianyun LI, Wenya WANG. Specific Emitter Identification Using Signal Trajectory Image[J]. Journal of Electronics & Information Technology, 2020, 42(4): 941-949. doi: 10.11999/JEIT190329
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The radio frequency fingerprinting of the emitter is complex, and the performance of Specific Emitter Identification (SEI) is subjected to the present expertise. To remedy this shortcoming, this paper presents a novel SEI algorithm based on signal trajectory image, which realizes joint extraction of multiple complex fingerprints using deep learning architecture. First, this paper analyses the visual characteristics of multiple emitter imperfections in the signal trajectory image. Thereafter, signal trajectory grayscale image is used as the signal representation. Finally, a deep residual network is constructed to learn the visual characteristics reflected in the images. The proposed method overcomes the limitations of existing knowledge, and combines high information integrity with low computational complexity. Simulation results demonstrate that, compared with the existing algorithms, the proposed one can remarkably improve the SEI performance with a gain of about 30%.
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DANEV B, ZANETTI D, and CAPKUN S. On physical-layer identification of wireless devices[J]. ACM Computing Surveys, 2012, 45(1): 1–29. doi: 10.1145/2379776.2379782
|
|
SPEZIO A E. Electronic warfare systems[J]. IEEE Transactions on Microwave Theory and Techniques, 2002, 50(3): 633–644. doi: 10.1109/22.989948
|
|
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): 160–167. doi: 10.1109/JSTSP.2018.2796446
|
|
HAN Jie, ZHANG Tao, REN Dongfang, et al. Communication emitter identification based on distribution of bispectrum amplitude and phase[J]. IET Science, Measurement & Technology, 2017, 11(8): 1104–1112. doi: 10.1049/iet-smt.2017.0024
|
|
BERTONCINI C, RUDD K, NOUSAIN B, et al. Wavelet fingerprinting of radio-frequency identification (RFID) tags[J]. IEEE Transactions on Industrial Electronics, 2012, 59(12): 4843–4850. doi: 10.1109/TIE.2011.2179276
|
|
ZHANG Jingwen, WANG Fanggang, DOBRE O A, et al. Specific emitter identification via Hilbert-Huang transform in single-hop and relaying scenarios[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(6): 1192–1205. doi: 10.1109/TIFS.2016.2520908
|
|
SATIJA U, TRIVEDI N, BISWAL G, et al. Specific emitter identification based on variational mode decomposition and spectral features in single hop and relaying scenarios[J]. IEEE Transactions on Information Forensics and Security, 2019, 14(3): 581–591. doi: 10.1109/TIFS.2018.2855665
|
|
BRIK V, BANERJEE S, GRUTESER M, et al. Wireless device identification with radiometric signatures[C]. The 14th ACM International Conference on Mobile Computing and Networking, San Francisco, USA, 2008: 116–127.
|
|
HUANG Yuanling and ZHENG Hui. Radio frequency fingerprinting based on the constellation errors[C]. The 18th Asia-Pacific Conference on Communications, Jeju Island, South Korea, 2012: 900–905.
|
|
彭林宁, 胡爱群, 朱长明, 等. 基于星座轨迹图的射频指纹提取方法[J]. 信息安全学报, 2016, 1(1): 50–58. doi: 10.19363/j.cnki.cn10-1380/tn.2016.01.007
PENG Linning, HU Aiqun, ZHU Changming, et al. Radio fingerprint extraction based on constellation trace figure[J]. Journal of Cyber Security, 2016, 1(1): 50–58. doi: 10.19363/j.cnki.cn10-1380/tn.2016.01.007
|
|
O’SHEA T J, ROY T, and CLANCY T C. Over-the-air deep learning based radio signal classification[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1): 168–179. doi: 10.1109/JSTSP.2018.2797022
|
|
KULIN M, KAZAZ T, MOERMAN I, et al. End-to-end learning from spectrum data: A deep learning approach for wireless signal identification in spectrum monitoring applications[J]. IEEE Access, 2018, 6: 18484–18501. doi: 10.1109/ACCESS.2018.2818794
|
|
RIYAZ S, SANKHE K, IOANNIDIS S, et al. Deep learning convolutional neural networks for radio identification[J]. IEEE Communications Magazine, 2018, 56(9): 146–152. doi: 10.1109/MCOM.2018.1800153
|
|
DING Lida, WANG Shilian, WANG Fanggang, et al. Specific emitter identification via convolutional neural networks[J]. IEEE Communications Letters, 2018, 22(12): 2591–2594. doi: 10.1109/LCOMM.2018.2871465
|
|
Agilent Technologies. Agilent technologies wireless test solutions application note 1313: Testing and troubleshooting digital RF communications transmitter designs[EB/OL]. http://literature.cdn.keysight.com/litweb/pdf/5968-3578E.pdf, 2016.
|
|
SRIDHARAN G. Phase noise in multi-carrier systems[D]. [Master dissertation], University of Toronto, 2010: 9–44.
|
|
HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778.
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