Specific Emitter Identification Based on Regression Analysis Theory

ZHAO Yaqin; YANG Rongqian; WU Longwen; HE Shengyang; NIU Jinpeng; ZHAO Liang

doi:10.11999/JEIT220190

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1227-1235

ZHAO Yaqin, YANG Rongqian, WU Longwen, HE Shengyang, NIU Jinpeng, ZHAO Liang. Specific Emitter Identification Based on Regression Analysis Theory[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1227-1235. doi: 10.11999/JEIT220190

Citation:

ZHAO Yaqin, YANG Rongqian, WU Longwen, HE Shengyang, NIU Jinpeng, ZHAO Liang. Specific Emitter Identification Based on Regression Analysis Theory[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1227-1235. doi: 10.11999/JEIT220190

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Specific Emitter Identification Based on Regression Analysis Theory

doi: 10.11999/JEIT220190

1.
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2.
Beijing Research Institute, Huawei Technology Limited Company, Beijing 100085, China
3.
Institute of Industrial and Electrical Testing, China Railway Harbin Group Co. Ltd., Harbin 150001, China

Funds: The National Natural Science Foundation of China (61671185, 62071153)

Received Date: 2022-02-25
Accepted Date: 2022-09-06
Rev Recd Date: 2022-08-29

Available Online: 2022-09-09

Publish Date: 2023-04-10

Abstract

Abstract

Focusing on the problem that the signal characteristics are not related to the hardware composition in the specific emitter identification, two feature extraction methods are used in this paper: high-order spectral analysis and Variational Modal Decomposition (VMD) for research and analysis. The surrounding-line bispectral integration and improved VMD technology are used to extract and analyze the features of the hardware in the semi-physical simulation signal and Advanced Design System (ADS) output signal. Through ADS, the influence of emitter phase noise and nonlinear distortion of power amplifier on signal unintentional modulation characteristics is quantitatively analyzed, the correlation of variables is analyzed, and the significantly related variables are regressed and fitted to obtain their correlation regression function. Using the correlation between hardware and features, the traditional Support Vector Machines (SVM) classifier is improved to construct a correlation weight SVM classifier. Finally, the results show that the classification accuracy of weighted SVM is improved by more than 10% compared with single core SVM under the same signal-to-noise ratio.
- Specific emitter identification,
- Feature extraction,
- Regression analysis,
- Weighted Support Vector Machine (SVM)

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References(15)

References

[1]	DONG Zeyu, LV Fengrong, WAN Tao, et al. Radar signal modulation recognition based on bispectrum features and deep learning[C]. 2021 International Conference on Computer Engineering and Application (ICCEA), Kunming, China, 2021: 63–67.
[2]	关键, 伍僖杰, 丁昊, 等. 基于对角积分双谱的海面慢速小目标检测方法[J]. 电子与信息学报, 2022, 44(7): 2449–2460. doi: 10.11999/JEIT210408 GUAN Jian, WU Xijie, DING Hao, et al. A method for detecting small slow targets in sea surface based on diagonal integrated bispectrum[J]. Journal of Electronics &Information Technology, 2022, 44(7): 2449–2460. doi: 10.11999/JEIT210408
[3]	LIN Yun, JIA Jicheng, WANG Sen, et al. Wireless device identification based on radio frequency fingerprint features[C]. 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020: 1–6.
[4]	DRAGOMIRETSKIY K and ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531–544. doi: 10.1109/TSP.2013.2288675
[5]	李晟, 徐飞洋, 李玉晓, 等. 基于变分模式分解和门循环单元的电子系统间歇故障严重程度评估方法[J]. 电子与信息学报, 2022, 44(10): 3673–3682. doi: 10.11999/JEIT210795 LI Sheng, XU Feiyang, LI Yuxiao, et al. A method for evaluating the severity of intermittent faults of electronic systems based on VMD-GRU[J]. Journal of Electronics &Information Technology, 2022, 44(10): 3673–3682. doi: 10.11999/JEIT210795
[6]	GOK G, ALP Y K, and ARIKAN O. A new method for specific emitter identification with results on real radar measurements[J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 3335–3346. doi: 10.1109/TIFS.2020.2988558
[7]	GOK G, ALP Y K, and ALTIPARMAK F. Radar fingerprint extraction via variational mode decomposition[C]. The 25th Signal Processing and Communications Applications Conference (SIU), Antalya, Turkey, 2017: 1–4.
[8]	CHEN Shiqian, DONG Xingjian, PENG Zhike, et al. Nonlinear chirp mode decomposition: A variational method[J]. IEEE Transactions on Signal Processing, 2017, 65(22): 6024–6037. doi: 10.1109/TSP.2017.2731300
[9]	LIU Mingwei and DOHERTY J F. Specific emitter identification using nonlinear device estimation[C]. 2008 IEEE Sarnoff Symposium, Princeton, USA, 2008: 1–5.
[10]	POLAK A C and GOECKEL D L. Wireless device identification based on RF oscillator imperfections[C]. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, 2014: 2679–2683.
[11]	RISQIWATI D, WIBAWA A D, PANE E S, et al. Feature selection for EEG-based fatigue analysis using pearson correlation[C]. 2020 International Seminar on Intelligent Technology and Its Applications (ISITIA), Surabaya, Indonesia, 2020: 164–169.
[12]	董小伟, 韩悦, 张正, 等. 基于多尺度加权特征融合网络的地铁行人目标检测算法[J]. 电子与信息学报, 2021, 43(7): 2113–2120. doi: 10.11999/JEIT200450 DONG Xiaowei, HAN Yue, ZHANG Zheng, et al. Metro pedestrian detection algorithm based on multi-scale weighted feature fusion network[J]. Journal of Electronics &Information Technology, 2021, 43(7): 2113–2120. doi: 10.11999/JEIT200450
[13]	HUANG Jing, ZHOU Jinzhi, and ZHENG Linwen. Support vector machine classification algorithm based on relief-F feature weighting[C]. 2020 International Conference on Computer Engineering and Application (ICCEA), Guangzhou, China, 2020: 547–553.
[14]	陈韬伟, 金炜东, 李杰. 基于围线积分双谱的雷达辐射源信号个体特征提取[J]. 计算机工程与应用, 2013, 49(8): 209–212,252. doi: 10.3778/j.issn.1002-8331.1109-0034 CHEN Taowei, JIN Weidong, and LI Jie. Individual feature extraction from radar emitter signals based on surrounding-line integral bispectrum[J]. Computer Engineering and Applications, 2013, 49(8): 209–212,252. doi: 10.3778/j.issn.1002-8331.1109-0034
[15]	罗亦泳, 姚宜斌, 黄城, 等. 基于改进VMD的变形特征提取与分析[J]. 武汉大学学报:信息科学版, 2020, 45(4): 612–619. doi: 10.13203/j.whugis20180286 LUO Yiyong, YAO Yibin, HUANG Cheng, et al. Deformation feature extraction and analysis based on improved variational mode decomposition[J]. Geomatics and Information Science of Wuhan University, 2020, 45(4): 612–619. doi: 10.13203/j.whugis20180286