Optical down-conversion signal separation method based on VMD adaptive modal recombination
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摘要: 光学下变频技术可将宽频带内全部电磁信号同时下变频到低频区间进行接收，是一种新型宽频带电磁环境快速接收技术。但是，获取的光学下变频信号中包含源个数未知、带宽不同的多种信号，现有信号分离方法需要获知源信号的个数，且无法同时分离窄带信号和宽带信号。为实现对光学下变频信号的自动分离，提出了一种基于变分模态分解（VMD）自适应模态重组的光学下变频信号分离方法。通过频谱分割因子和频谱包络检测，对光学下变频信号的VMD过分解模态进行自动重组和信号重组模态提取，实现光学下变频信号分离。对于包含普通脉冲信号、宽带码分多址（WCDMA）信号和线性调频脉冲信号的光学下变频信号，可自动实现对三种信号的分离，且与原信号的相似系数均高于0.97。实验结果表明，所提及方法在分离光学下变频信号时无需获知源信号的个数，并能同时分离具有不同带宽的多种源信号。Abstract: Optical down-conversion technology can simultaneously down-convert all electromagnetic signals within a wide frequency band to the low-frequency range for reception, and is a new type of fast reception technology for broadband electromagnetic environments. However, the obtained optical down-conversion signal contains multiple signals with unknown number of sources and different bandwidths. Existing signal separation methods need to know the number of source signals and cannot simultaneously separate narrowband and broadband signals. To achieve automatic separation of optical down-conversion signals, a method for optical down-conversion signal separation based on VMD adaptive mode recombination is proposed. By using spectral segmentation factors and spectral envelope detection, the VMD over decomposition modes of optical down-conversion signals are automatically recombined and signal recombination modes are extracted, achieving the separation of optical down-conversion signals. For optical down-conversion signals containing ordinary pulse signals, WCDMA signals, and linear frequency modulation pulse signals, this method can automatically separate the three types of signals, and the similarity coefficients with the original signal are all higher than 0.97. The experimental results show that the method proposed in this paper does not need to know the number of source signals when separating optical down-conversion signals, and can simultaneously separate multiple source signals with different bandwidths.
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图 4 无信号时的光学下变频系统输出
Figure 4. Optical down-conversion system output when no signal is detected
图 5 探测到3种信号时的光学下变频系统输出
Figure 5. Optical down-conversion system output when three types of signals are detected
图 8 本文方法分离后的光学下变频信号
Figure 8. Optical down-conversion signal separated by the method proposed in this article
表 1 分离后光学下变频信号与原信号的相关系数
Table 1. Correlation coefficient between the separated optical down-conversion signal and the original signal
correlation coefficient (LFM) correlation coefficient (NP) correlation coefficient (WCDMA) 0.9721 0.9721 0.9754 
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[1] Xie Shuguo, Wang Tianheng, Hao Xuchun, et al. Localization and frequency identification of large-range wide-band electromagnetic interference sources in electromagnetic imaging system[J]. Electronics, 2019, 8: 499. doi: 10.3390/electronics8050499 [2] Yang Yan, Xie Shuguo, Wang Tianheng, et al. Multi-frequency electric field measurement method for optical under-sampling system[J]. IEEE Sensors Journal, 2021, 21(20): 23024-23036. doi: 10.1109/JSEN.2021.3105275 [3] 毋文峰, 陈小虎, 苏勋家. 基于经验模式分解的单通道机械信号盲分离[J]. 机械工程学报, 2011, 47(4):12-16 doi: 10.3901/JME.2011.04.012Wu Wenfeng, Chen Xiaohu, Su Xunjia. Blind source separation of single-channel mechanical signal based on empirical mode decomposition[J]. Journal of Mechanical Engineering, 2011, 47(4): 12-16 doi: 10.3901/JME.2011.04.012 [4] 张袁元, 李舜酩, 胡伊贤, 等. LMS方法的改进及联合EEMD在振动信号去噪中的应用[J]. 振动与冲击, 2013, 32(20):61-66Zhang Yuanyuan, Li Shunming, Hu Yixian, et al. Improvement of LMS method and its application combined with EEMD in vehicle vibration signal denoising[J]. Journal of Vibration and Shock, 2013, 32(20): 61-66 [5] 姜绍飞, 陈志刚, 沈清华, 等. 基于EEMD与FastICA的损伤异常识别与定位[J]. 振动与冲击, 2016, 35(1):203-209Jiang Shaofei, Chen Zhigang, Shen Qinghua, et al. Damage detection and location based on EEMD-Fast ICA algorithm[J]. Journal of Vibration and Shock, 2016, 35(1): 203-209 [6] 王小伟. 经验模态分解在单通道语音盲分离中的应用研究[D]. 成都: 西南交通大学, 2014Wang Xiaowei. The application of EMD in the single channel speech blind separation[D]. Chengdu: Southwest Jiaotong University, 2014 [7] 马增强, 柳晓云, 张俊甲, 等. VMD和ICA联合降噪方法在轴承故障诊断中的应用[J]. 振动与冲击, 2017, 36(13):201-207Ma Zengqiang, Liu Xiaoyun, Zhang Junjia, et al. Application of VMD-ICA combined method in fault diagnosis of rolling bearings[J]. Journal of Vibration and Shock, 2017, 36(13): 201-207 [8] Dey P, Satija U, Ramkumar B. Single channel blind source separation based on variational mode decomposition and PCA[C]//2015 Annual IEEE: India Conference[C], . 2015: 1-5. [9] Zhang Ya’nan, Qi Shengbo, Zhou Lin. Single channel blind source separation for wind turbine aeroacoustics signals based on variational mode decomposition[J]. IEEE Access, 2018, 6: 73952-73964. doi: 10.1109/ACCESS.2018.2884035 [10] 赵知劲, 黄艳波, 强芳芳, 等. 基于反馈变分模式分解的单通道盲源分离算法[J]. 振动与冲击, 2019, 38(13):268-273Zhao Zhijin, Huang Yanbo, Qiang Fangfang, et al. Single channel blind source separation algorithm based on feedback variational mode decomposition[J]. Journal of Vibration and Shock, 2019, 38(13): 268-273 [11] 王康, 程浩, 张坤. 基于变分模态分解的单通道信号盲源分离方法[J]. 科技通报, 2019, 35(2):138-143,149Wang Kang, Cheng Hao, Zhang Kun. Blind source separation of single-channel signal based on variation mode decomposition[J]. Bulletin of Science and Technology, 2019, 35(2): 138-143,149 [12] 程秋冰. 雷达信号分离与干扰识别技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2021Cheng Qiubing. Research on radar signal separation and interference signal recognition technology[D]. Harbin: Harbin Engineering University, 2021 [13] 张晨阳, 张亚, 李世中. 基于变分模态分解的侵彻过载信号盲分离[J]. 振动与冲击, 2022, 41(5):280-286Zhang Chenyang, Zhang Ya, Li Shizhong. Blind separation of penetration overload signals based on VMD[J]. Journal of Vibration and Shock, 2022, 41(5): 280-286 [14] 甄龙信, 任良, 董前程. 基于单通道盲源分离的结构模态参数识别[J]. 振动与冲击, 2023, 42(11):252-261,294Zhen Longxin, Ren Liang, Dong Qiancheng. Structural modal parameter identification based on single channel blind source separation[J]. Journal of Vibration and Shock, 2023, 42(11): 252-261,294 [15] Dragomiretskiy K, Zosso D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544. doi: 10.1109/TSP.2013.2288675 -
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