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采用重构吸引子的辐射源个体识别技术

Specific emitter identification using reconstructed attractors
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摘要 为解决现有基于相空间个体识别方法面临重构特征矢量维数高、计算效率低、鲁棒性差等问题,从非线性动力学角度出发,构建了基于重构吸引子的辐射源个体识别框架,并在此框架内提出了基于等距映射的辐射源个体识别技术。该技术采用等距映射从相空间中重构辐射源吸引子,可以更低的维度描述辐射源系统动力学特性,反映辐射源个体的“指纹”特征。实验表明该方法识别准确率更高、效率更高、听鲁棒性更强。 In order to solve the problems of high dimension of reconstructed feature vector,low computational efficiency and poor robustness of existing phase space based individual recognition methods,SEI(specific emitter identification)framework based on reconstructed attractors was proposed from the perspective of nonlinear dynamics.Within the proposed framework,a novel SEI technology based on Isomap(isometric mapping)was developed.The technology used Isomap to reconstruct the emitter attractor from phase space,which can describe the dynamic characteristics of the emitter system in a lower dimension and reflect the“fingerprint”characteristics of the emitter individual.Experiments show that the proposed method can achieve higher accuracy,higher efficiency and better robustness.
作者 赵雨睿 宋川江 王翔 黄知涛 ZHAO Yurui;SONG Chuanjiang;WANG Xiang;HUANG Zhitao(College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China;College of Electronic Engineering,National University of Defense Technology,Hefei 230037,China)
出处 《国防科技大学学报》 EI CAS CSCD 北大核心 2023年第5期12-20,共9页 Journal of National University of Defense Technology
基金 国家自然科学基金面上资助项目(62271494) 国防科技大学青年创新奖资助项目(18/19-QNCXJ)。
关键词 辐射源个体识别 吸引子 等距映射 非线性动力学 individual identification of specific emitter attractor Isomap nonlinear dynamics
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  • 1郑新,李文辉,潘厚忠.雷达发射机技术[M].北京:电子工业出版社,2004
  • 2Carroll T L. A Nonlinear Dynamics Method for Signal Identification[J] .Chaos 17,023109- 1,2007.
  • 3Grassberger P, Procaccia I. Measuring the Strangeness of Strange Attractors[J]. Physica D, 1983, 9: 189-208.
  • 4Kantz H, Thomas S. Nonlinear Time Series Analysis[M]. Cambridge: Cambridge University Press, 1997.
  • 5Ye J, Li Q, Xiong H, et al. IDR/QR: An Incremental Dimension Reduction Algorithm via QR Decomposition[C]//The Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2004.
  • 6Ye J, Li Q. LDA/QR: An Efficient and Effective Dimension Reduction Algorithm and Its Theoretical Foundation[J]. Pattern Recognition, 2004,37 (4) :851 - 854.
  • 7Povinelli R J, Johnson M T, Lindgren A C. Statistical Models of Reconstructed Phase Spaces for Signal Classification[J]. IEEE Trans. Signal Processing, June, 2006,54:2178-2186.
  • 8许丹,姜文利,周一宇.雷达功放正弦激励下的无意调制特征分析[J].系统工程与电子技术,2008,30(3):400-403. 被引量:11
  • 9许丹,柳征,姜文利,周一宇.窄带信号中的放大器“指纹”特征提取:原理分析及FM广播实测实验[J].电子学报,2008,36(5):927-932. 被引量:27
  • 10田金鹏,刘燕平,刘小娟.基于瞬态强度的射频指纹识别方法[J].电子测量技术,2016,39(4):58-61. 被引量:4

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