基于相空间重构的辐射源个体识别技术综述

赵雨睿; 黄知涛; 王翔

doi:10.12000/JR23057

基于相空间重构的辐射源个体识别技术综述

doi: 10.12000/JR23057

国防科技大学电子科学学院长沙 410073

基金项目: 国家自然科学基金(62271494)

详细信息

作者简介:
赵雨睿，博士生，主要研究方向为辐射源个体识别技术、深度学习技术

黄知涛，博士，教授，主要研究方向为非合作信号处理、目标识别

王　翔，博士，副教授，主要研究方向为通信信号处理、深度学习

通讯作者:
黄知涛 huangzhitao@nudt.edu.cn

王翔 christopherwx@163.com

责任主编：雷迎科 Corresponding Editor: LEI Yingke
中图分类号: TN95
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出版历程
- 收稿日期: 2023-04-26
- 修回日期: 2023-06-29
- 网络出版日期: 2023-07-19
- 刊出日期: 2023-08-28

A Review of Specific Emitter Identification Based on Phase Space Reconstruction

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (62271494)

More Information

Corresponding author: HUANG Zhitao, huangzhitao@nudt.edu.cn; WANG Xiang, christopherwx@163.com

摘要

摘要: 辐射源个体识别技术，起源于雷达目标精确辨识任务，旨在根据截获的电磁信号提取辐射源独有的指纹特征，并进一步辨识辐射源个体身份的技术。相空间重构技术，作为一种有效的时间序列分析技术，可以从一维时间序列中重构一个与原系统非线性动力学特性相同的相空间。相空间重构技术自2007年开始被诸多学者引入辐射源个体识别问题中。然而，该项技术研究时间较短且分布较为分散，尚未形成清楚的发展脉络。对此，该文旨在系统性地总结归纳基于相空间重构的辐射源个体识别技术。首先，在介绍相空间重构技术的基础上，论述了相空间重构技术应用于辐射源个体识别的理论依据。其次，从方法框架、算法分类、算法应用效果、算法初步对比4个维度，介绍了基于相空间重构技术的辐射源个体识别技术的研究现状。仿真实验结果表明，该项技术能够有效地捕捉辐射源硬件的非理想性，胜任目标精确辨识任务，并可通过特征融合等手段提升算法鲁棒性。最后，总结现有方法的不足并展望其未来发展前景。
- 辐射源个体识别 /
- 相空间重构技术 /
- 非线性动力学 /
- 指纹特征 /
- 目标识别
Abstract: Specific Emitter Identification (SEI), originated from identifying radar systems, is to extract fingerprint features from the intercepted signals for recognizing emitter identifies. Phase Space Reconstruction (PSR) is a powerful technique in time series analysis that can reconstruct a phase space from a one-dimensional time series, preserving the nonlinear dynamic characteristics of the original system. The integration of phase space reconstruction into SEI began in 2007. However, due to the recent and diverse nature of research focused on PSR-based SEI methods, it is challenging to establish a clear context for its development. To address this issue, this paper aims to systematically summarize SEI methods based on phase space reconstruction. First, we introduce phase space reconstruction technology and emphasize the necessity and feasibility of applying it in SEI. Next, we present a comprehensive framework, classification, application, and comparison of PSR-based SEI methods. Simulation experiments demonstrated that PSR-based SEI methods can effectively describe the non-idealities of emitter hardware components and accomplish the target identification task. In addition, we verify that feature fusion enhances the algorithm’s robustness. Finally, we summarize the limitations of existing methods and outline prospects for future development.
- Specific Emitter Identification (SEI) /
- Phase Space Reconstruction (PSR) /
- Nonlinear dynamics /
- Fingerprint features /
- Target identification

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图 1 辐射源个体识别系统流程

Figure 1. Flowchart of Specific Emitter Identification (SEI)

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图 2 基于相空间重构的辐射源个体识别方法框架

Figure 2. Framework of Specific Emitter Identification based on Phase Space Reconstruction (SEI-PSR)

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图 3 基于逐点对齐点云深度学习网络的辐射源个体识别流程^[52]

Figure 3. SEI based on Alignment Point by Point-based PointNet (APBP-PointNet)^[52]

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图 4 基于逐点对齐点云深度学习网络结构^[52]

Figure 4. Structure of the APBP-PointNet^[52]

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图 5 基于重构吸引子辐射源个体识别框架^[53]

Figure 5. SEI based on the reconstructed attractor^[53]

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图 6 基于多尺度流形特征的辐射源个体识别方法^[54]

Figure 6. SEI based on multi-level manifold features^[54]

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图 7 实验中4部雷达时域脉冲信号^[20]

Figure 7. Pulse waveforms of four radars^[20]

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图 8 实验中采用AKDS700装置图^[55]

Figure 8. The AKDS700 device^[55]

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图 9 直接序列扩频信号的时域波形^[45]

Figure 9. Waveforms of Direct Sequence Spread-Spectrum (DSSS) signals^[45]

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图 10 不同信噪比下的分类识别效果^[49]

Figure 10. Recognition results of different SNR^[49]

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图 11 无线网卡信号指纹特征^[48]

Figure 11. Fingerprint features of wireless network cards^[48]

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图 12 对讲机信号指纹特征^[48]

Figure 12. Fingerprint features of interphones^[48]

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图 13 手机型号与厂商^[52]

Figure 13. Manufactures and models of mobile phones^[52]

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图 14 手机数据集上的识别率^[52]

Figure 14. Classification accuracy of mobile phones^[52]

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图 15 实测信号样本功率谱图^[51]

Figure 15. Power spectrum of collected signals^[51]

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图 16 实测信号识别率随信噪比变化曲线^[51]

Figure 16. Recognition results of different SNR^[51]

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图 17 采用不同功率放大器的自制电路示意图^[17]

Figure 17. Schematic of the experiment with various amplifiers^[17]

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图 18 不同功率放大器对应的相空间微分分布^[17]

Figure 18. Distributions of the phase space difference according to various amplifiers^[17]

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图 19 不同硬件非理想性下信号重构相空间的可视化

Figure 19. Reconstructed phase space of different hardware imperfections

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图 20 不同数据集下算法的识别准确率

Figure 20. Classification accuracy for different datasets

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图 21 不同信噪比下算法识别准确率

Figure 21. Classification accuracy with different SNRs

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表 1 基于重构相空间的辐射源个体识别指纹特征总结

Table 1. Summary of the Specific Emitter Identification (SEI) methods based on Phase Space Reconstruction (PSR)

分析角度	特征	描述意义	参考文献
统计特性	关联维数	时间序列的复杂性和自相似性	文献[18,19,41,42]
	最大Lyapunov指数	相邻轨迹的平均指数发散率	文献[18,19,41,42]
	Kolmogorov熵	混动系统的结构复杂程度	文献[18,41,42]
	排列熵	描述时间序列的复杂程度	文献[43–45]
	近似熵	时间序列波动的规律性和不可预测性	文献[43,46]
	样本熵	时间序列波动的规律性和不可预测性	文献[43]
矩阵固有特性	奇异值分解(SVD)	矩阵特征值	文献[46–48]
矩阵固有特性	Low-rank子空间	矩阵低秩表示	文献[49]
几何构型	相点分布	相点概率密度分布	文献[48]
	质心	重构相空间质心分布	文献[50]
	交叉关联积分	相点概率密度分布	文献[51]
	几何特征	重构相空间的几何特征	文献[20]
转移特性	相点差分方法	状态矢量的时间微分	文献[17]
转移特性	相点转移角度与距离	状态转移规律	文献[47,48]
系统等效性	直接识别	状态分布及其转移规律	文献[52]
系统等效性	重构系统吸引子	系统复杂度	文献[53,54]

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表 2 基于重构相空间的辐射源个体识别技术在不同数据集上的应用总结

Table 2. Summary of the SEI-PSR applications on various datasets

设备	文献
雷达	文献[19,20,50]
高速扩频电台	文献[45]
跳频电台	文献[49]
通用软件无线电	文献[20,52–54]
无线网卡	文献[46–48]
对讲机	文献[48]
手机设备	文献[18,52]
舰船通信设备	文献[43]
任意波形发生器	文献[51]
自制电路	文献[17,41]

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表 3 CART决策树模型在重构相空间特征下的信号“指纹”识别率(%)

Table 3. Accuracy with the CART decision tree (%)

设备	识别率
雷达辐射源1	99.61
雷达辐射源2	95.89
雷达辐射源3	97.80
雷达辐射源4	94.93

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表 4 采用稳态信号进行识别的混淆矩阵(%)

Table 4. Classification confusion matrix of random payload data: Steady-state signal of radios (%)

真实标签	预测标签
真实标签	R1	R2	R3
R1	94.70	4.72	1.62
R2	2.15	90.91	2.81
R3	3.15	4.37	95.57

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表 5 采用握手信号进行识别的混淆矩阵(%)

Table 5. Classification confusion matrix of fixed payload data: Hand-shaking signal of radios (%)

真实标签	预测标签
真实标签	R1	R2	R3
R1	95.13	3.93	1.55
R2	2.37	91.08	3.02
R3	2.50	4.99	95.43

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表 6 不同信噪比下算法识别准确率

Table 6. Classification accuracy with different SNRs

信噪比(dB)	识别率(%)
50	100
40	100
30	100
20	98
10	95
0	75

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表 7 不同信号长度、不同待识别个体数目下，所提方法的正确识别率(%)

Table 7. The identification accuracy of proposed method with different signal length and individuals (%)

样本点数	个体数目
样本点数	3	4	5	6
500	94.72	92.31	89.31	83.25
600	94.78	93.36	90.96	86.03
700	95.56	92.85	92.77	88.12
800	96.50	95.76	93.85	90.04
900	97.22	96.41	94.73	91.78
1000	97.35	96.88	95.18	92.46

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