基于R半径DFT信号时频灰度特征的辐射源识别
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摘要
针对通信辐射源的个体识别问题,提出一种基于R半径离散傅里叶变换(DFT)对寄生调制信号的DFT采样圆内以及采样圆外零极点进行时频灰度特征提取的新方法。以零点特征提取为例,在寄生调制信号的幅频特性于零点处表现为谷值的基础上,结合R半径DFT变换,生成时频二维灰度图,并通过边缘检测对增强后的时频二维灰度图进行图像断点检测,以完成对寄生调制信号的特征提取,通过在不同模拟发射机下寄生调制信号所携带的零点位置半径不同,说明寄生调制信号发射自不同的辐射源。仿真实验结果表明,该方法提取出的两辐射源特征差异明显,稳定性高,可靠性好,能够快速有效地完成辐射源的个体识别。
Aiming at the problem of individual recognition of communication radiation source,a new method based on Rradius discrete Fourier transform(DFT) is proposed to extract the time-frequency grayscale features of the zero-pole points inside and outside of DFT sampling circle of the parasitic modulated signal. Taking zero-point feature extraction as an example,the R-radius DFT is combined to generate the time-frequency 2D grayscale image when the amplitude-frequency characteristics of the parasitic modulated signal are assumed as the valley value at zero point,and then the edge detection is used to perform the break points detection for the enhanced time-frequency 2D grayscale image,so as to complete the feature extraction of parasitic modulated signals. The radius of zero-point position carried by parasitic modulated signal is different while using different simulation transmitters,which can indicate that the parasitic modulated signals are emitted by different radiation sources. The simulation results show that the difference of the characteristics of the two radiation sources extracted by this method is obvious;the recognition method has high stability and reliability,and can accomplish the individual recognition of radiation source quickly and effectively.
Aiming at the problem of individual recognition of communication radiation source,a new method based on Rradius discrete Fourier transform(DFT) is proposed to extract the time-frequency grayscale features of the zero-pole points inside and outside of DFT sampling circle of the parasitic modulated signal. Taking zero-point feature extraction as an example,the R-radius DFT is combined to generate the time-frequency 2D grayscale image when the amplitude-frequency characteristics of the parasitic modulated signal are assumed as the valley value at zero point,and then the edge detection is used to perform the break points detection for the enhanced time-frequency 2D grayscale image,so as to complete the feature extraction of parasitic modulated signals. The radius of zero-point position carried by parasitic modulated signal is different while using different simulation transmitters,which can indicate that the parasitic modulated signals are emitted by different radiation sources. The simulation results show that the difference of the characteristics of the two radiation sources extracted by this method is obvious;the recognition method has high stability and reliability,and can accomplish the individual recognition of radiation source quickly and effectively.
引文
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[15]杨少奇,田波,李欣,等.基于时频图像特征提取的LFM雷达有源欺骗干扰识别[J].空军工程大学学报(自然科学版),2016,17(1):56-59.YANG Shaoqi,TIAN Bo,LI Xin,et al. A recognition method of LFM radar active deception jamming based on SPWVD figure[J]. Journal of Air Force Engineering University(natural science edition),2016,17(1):56-59.
[16]徐俊峰,郭海涛,张锐,等.一种结合四元数与直方图的多光谱图像自适应边缘检测方法[J].测绘科学技术学报,2015(1):61-65.XU Junfeng,GUO Haitao,ZHANG Rui,et al. An adaptive edge detection method for multi-spectral image combining qua-ternion and histogram[J]. Journal of geomatics science and technology,2015(1):61-65.
[2]牛莉敏,郑文秀,刘东杰.基于码元高斯拟合的PUE攻击检测策略[J].计算机应用研究,2017,34(6):1798-1800.NIU Limin,ZHENG Wenxiu,LIU Dongjie. PUE attack detec-tion method based on encode element Gauss fitting symbol[J].Application research of computers,2017,34(6):1798-1800.
[3]桂云川,杨俊安,吕季杰.基于固有时间尺度分解模型的通信辐射源特征提取算法[J].计算机应用研究,2017,34(4):1172-1175.GUI Yunchuan,YANG Jun’an,LüJijie. Feature extraction al-gorithm based on intrinsic time-scale decomposition model for communication transmitter[J]. Application research of compu-ters,2017,34(4):1172-1175.
[4]徐玉龙,王金明,徐志军,等.基于小波熵的辐射源指纹特征提取方法[J].数据采集与处理,2014,29(4):631-635.XU Yulong,WANG Jinming,XU Zhijun,et al. Fingerprint feature extraction method for emitters based on wavelet entropy[J]. Journal of data acquisition and processing,2014,29(4):631-635.
[5]梁江海,黄知涛,袁英俊,等.一种基于经验模态分解的通信辐射源个体识别方法[J].中国电子科学研究院学报,2013,8(4):393-397.LIANG Jianghai,HUANG Zhitao,YUAN Yingjun,et al. A method based on empirical mode decomposition for identifying transmitter individuals[J]. Journal of China Academy of Elec-tronics and Information Technology,2013,8(4):393-397.
[6]杨立波.雷达辐射源无意调制特征提取算法研究[D].成都:电子科技大学,2014.YANG Libo. Research on unintentional modulation feature ex-traction for radar emitter[D]. Chengdu:University of Electronic Science and Technology of China,2014.
[7]肖乐群.基于高阶统计量的雷达辐射源信号识别方法研究[D].郑州:解放军信息工程大学,2012.XIAO Lequn. Research on radar emitter signal recognition based on high order statistics[D]. Zhengzhou,China:Informa-tion Engineering University,2012.
[8]樊昌信,曹丽娜.通信原理[M].7版.北京:国防工业出版社,2013:188-190.FAN Changxin,CAO Lina. Principle of communication[M].7th ed. Beijing, China:National Defend Industry Press,2013:188-190.
[9]郑文秀.R半径DFT在系统盲辨识中的应用[J].电子信息对抗技术,2011(4):34-36.ZHENG Wenxiu. Application of the R-radius DFT in system′s blind identification[J]. Electronic information warfare technolo-gy,2011(4):34-36.
[10] WANG Song,HU Jiankun. A blind system identification ap-proach to cancelable fingerprint templates[J]. Pattern recogni-tion,2016,54(C):14-22.
[11] YU Chengpu,ZHANG Cishen,XIE Lihua. Blind identifica-tion of non-minimum phase ARMA systems[J]. Automatica,2013,49(6):1846-1854.
[12]王浩,张叶,沈宏海,等.图像增强算法综述[J].中国光学,2017,10(4):438-448.WANG Hao,ZHANG Ye,SHEN Honghai,et al. Review of image enhancement algorithms[J]. Chinese optics,2017,10(4):438-448.
[13]姜柏军,钟明霞.改进的直方图均衡化算法在图像增强中的应用[J].激光与红外,2014,44(6):702-706.JIANG Baijun,ZHONG Mingxia. Improved histogram equa-lization algorithm in the image enhancement[J]. Laser & infra-red,2014,44(6):702-706.
[14]白航,赵拥军,胡德秀.时频图像局部二值模式特征在雷达信号分类识别中的应用[J].宇航学报,2013,34(1):139-146.BAI Hang,ZHAO Yongjun,HU Dexiu. Radar signal recogni-tion based on the local binary pattern feature of time-frequency image[J]. Journal of astronautics,2013,34(1):139-146.
[15]杨少奇,田波,李欣,等.基于时频图像特征提取的LFM雷达有源欺骗干扰识别[J].空军工程大学学报(自然科学版),2016,17(1):56-59.YANG Shaoqi,TIAN Bo,LI Xin,et al. A recognition method of LFM radar active deception jamming based on SPWVD figure[J]. Journal of Air Force Engineering University(natural science edition),2016,17(1):56-59.
[16]徐俊峰,郭海涛,张锐,等.一种结合四元数与直方图的多光谱图像自适应边缘检测方法[J].测绘科学技术学报,2015(1):61-65.XU Junfeng,GUO Haitao,ZHANG Rui,et al. An adaptive edge detection method for multi-spectral image combining qua-ternion and histogram[J]. Journal of geomatics science and technology,2015(1):61-65.