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基于同步压缩小波变换的主信号抑制技术

吴龙文 牛金鹏 王昭 何胜阳 赵雅琴

引用本文: 吴龙文, 牛金鹏, 王昭, 何胜阳, 赵雅琴. 基于同步压缩小波变换的主信号抑制技术[J]. 电子与信息学报, doi: 10.11999/JEIT190650 shu
Citation:  Longwen WU, Jinpeng NIU, Zhao WANG, Shengyang HE, Yaqin ZHAO. Primary Signal Suppression Based on Synchrosqueezed Wavelet Transform[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190650 shu

基于同步压缩小波变换的主信号抑制技术

    作者简介: 吴龙文: 男,1988年生,工程师,研究方向为辐射源个体识别;
    牛金鹏: 男,1997年生,硕士生,研究方向为辐射源个体识别;
    王昭: 男,1995年生,工程师,研究方向为辐射源个体识别;
    何胜阳: 男,1983年生,高级工程师,研究方向为无线光通信;
    赵雅琴: 女,1976年生,教授,研究方向为辐射源识别和光通信
    通讯作者: 赵雅琴,yaqinzhao@hit.edu.cn
摘要: 在辐射源个体识别技术中,能量较高的主信号往往导致微弱个体特征稳定性降低,进而影响最终的个体识别效果。为了解决该问题并提升辐射源个体识别性能,该文提出基于同步压缩小波变换的主信号抑制技术。首先,利用静态小波变换完成对带噪信号的去噪预处理;然后,利用同步压缩小波变换完成对主信号的检测和抑制,并以均方根误差和皮尔逊相关系数为数值指标,验证算法的有效性;最后,在主信号抑制的基础上,利用分形理论中盒维数完成对信号的特征提取,并利用单核支持向量机验证进行个体识别性能。实验结果表明,与主信号抑制之前相比,主信号抑制算法下个体识别率提升了10%左右,验证了同步压缩小波变换的主信号抑制算法对辐射源个体识别率提升的有效性。

English

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  • 图 1  SWT分解过程示意图

    图 2  LFM信号下SST主信号抑制效果仿真

    图 3  SST主信号抑制仿真(扩大相位噪声频1偏后)

    图 4  LFM信号源个体分形盒维数特征识别结果

    图 5  实测数据特征规范化后特征分布

    表 1  加性相位噪声参数

    辐射源个体与频偏对应的相位噪声幅度(信相噪比/(dB)
    f1=±2.75 MHzf2=±2.80 MHzf3=±3.10 MHz
    E111.989712.781515.7918
    E210.484511.672216.1877
    E3f21=±2.8 MHzf22=±2.9 MHzf23=±3.15 MHz
    12.781514.030816.1394
    下载: 导出CSV

    表 2  实测数据特征结构与来源

    特征序号特征来源
    1–4RF-DNA[19]
    4–8IMF-DNA[20]
    9–12BCD[18]
    13–20SIB[21]
    下载: 导出CSV
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