A recognition method of electromagnetic pulse based on ensemble learning of adaptive signal decomposition features

CHENG Lu1,2,3, WANG Xueqing1,2,3, LIU Yan3, CHENG Xianyou4, XU Xin1,2,3, JI Yicai1,2,3, FANG Guangyou1,2,3

Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (8) : 275-281.

PDF(2089 KB)
PDF(2089 KB)
Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (8) : 275-281.

A recognition method of electromagnetic pulse based on ensemble learning of adaptive signal decomposition features

  • CHENG Lu1,2,3, WANG Xueqing1,2,3, LIU Yan3, CHENG Xianyou4, XU Xin1,2,3, JI Yicai1,2,3, FANG Guangyou1,2,3
Author information +
History +

Abstract

In order to accurately and efficiently identify nuclear electromagnetic pulse (NEMP) and lightning electromagnetic pulse (LEMP), a recognition and classification method based on adaptive signal decomposition and ensemble learning was proposed. First, for the problem of sample imbalance, data enhancement methods were used to preprocess the data set. In addition, Hilbert-Huang transform was applied to perform adaptive signal decomposition on NEMP and LEMP respectively. Then, features of the decomposed signal in the time domain, frequency domain and wavelet domain were extracted. Finally, the ensemble learning algorithm was used to identify and classify the extracted features. Experimental results show that the accuracy of the method on the measured data can reach more than 99.99%, and the false alarm rate of LEMP signals is less than one in ten thousand.

Key words

NEMP / LEMP / Hilbert-Huang transform / ensemble learning

Cite this article

Download Citations
CHENG Lu1,2,3, WANG Xueqing1,2,3, LIU Yan3, CHENG Xianyou4, XU Xin1,2,3, JI Yicai1,2,3, FANG Guangyou1,2,3. A recognition method of electromagnetic pulse based on ensemble learning of adaptive signal decomposition features[J]. Journal of Vibration and Shock, 2023, 42(8): 275-281

References

[1] 祁树锋,李夕海,韩绍卿,等. 基于多重分形分析的核爆与雷电电磁脉冲识别[J]. 振动与冲击,2013,32(07):8-10.
QI Shu-feng,LI Xi-hai, HAN Shaoqing, et al. Discrimination of nuclear explosion and lightning electromagnetic pulses using multi-fractal analysis[J]. Journal of Vibration and Shock, 2013,32(07):8-10.
[2] 祁树锋,李夕海,韩绍卿,等.核爆与雷电电磁脉冲识别[J].强激光与粒子束,2012,24(12):2935-2940.
QI Shu-feng, LI Xi-ha, HAN Shao-qing, et al. Discrimination of nuclear-explosion and lightning electromagnetic pulse [J]. High Power Laser and Particle Beams, 2012,24(12):2935-2940.
[3] 张旭荣, 张妙兰,刘新中. 小波变换在核爆电磁脉冲信号识别中的应用[J]. 电子与信息学报, 1999, 21(5):710-712.
ZHANG Xu-rong, ZHANG Miao-lan, LIU Xin-zhong. Studies of recognition methods of nuclear and lightning pulse signals with applications of wavelet transform[J]. Journal of Electronics, 1999,21(5):710-712.
[4] 张仲山,张恩山,高春霞. 小波分析在核爆与闪电识别中的应用[J]. 电波科学学报, 2000, 15(4):387-391.
ZHANG Zhong-shan, ZHANG En-shan, GAO Chun-xia. The application of wavelet analysis to Recognition of nuclear explosion and lightning[J]. Radio Science, 2000,15(4):387-391.
[5] Peng L ,  Yi Z ,  Chao H , et al. Lightning and nuclear explosion pattern recognition from optical and electromagnetic data[C]// 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2014.
[6] 李鹏,宋立军,韩超,等. 基于AR模型与神经网络的核爆与闪电脉冲信号识别[J].强激光与粒子束,2010,22(12):3052-3056.
LI Peng, SONG Li-jun, HAN Chao, et al. Recognition of NEMP and LEMP signals based on auto-regression model and artificial neutral network[J]. High Power Laser and Particle Beams, 2010,22(12):3052-3056.
[7] G. Yaqin, Support Vectors Classification Method Based on Matrix Exponent Boundary Fisher Projection, 2019 IEEE International Conference on Mechatronics and Automation (ICMA), 2019, pp. 957-961, doi: 10.1109/ICMA.2019.88162
41.
[8] 刘志刚,刘代志,孙新利. 核爆电磁脉冲信号的时变谱特征分析[J].核电子学与探测技术,2002(06):492-495+511.
LIU Zhi-gang, LIU Dai-zhi, SUN Xin-li. On the applications of time-frequency analysis in NEMP signal processing[J]. Nuclear Electronics and Detection Technology, 2002(06):492
-495+511.
[9] 李夕海,刘志刚,武红霞,等. 核爆电磁脉冲信号的分形特征分析[J].核电子学与探测技术, 2004(02):143-146.
LI Xi-hai, LIU Zhi-gang, WU Hong-xia, et al. On the application of fractal analysis and wavelet in NEMP signal processing[J]. Nuclear Electronics and Detection Technology, 2004,24(2):143-146.
[10] 祁树锋,李夕海,韩绍卿,等. 基于Hilbert谱区域能量比的核爆与雷电电磁脉冲识别[J].振动与冲击,2013,32(03):163-166.
QI Shu-feng, LI Xi-hai, HAN Shao-qing, et al. Discrimination of nuclear explosion and lightning electromagnetic pulse using regional energy ratio of Hilbert spectrum[J]. Journal of Vibration and Shock, 2013,32(03):163-166.
[11] 王涛,陈福贵,刘明,等. 基于时域特征分布的核爆电磁脉冲识别算法[J].振动与冲击,2020,39(08):159-164.
WANG Tao, CHEN Fu-gui, LIU Ming, et al. A discrimination algorithm of nuclear electromagnetic pulse based on characteristic parameters in time domain[J]. Journal of Vibration and Shock, 2020,39(08):159-164.
[12] 王浩骅,苗家友,朱万华,等. 神经网络识别算法去除核爆电磁脉冲探测闪电干扰[J].核电子学与探测技术,2021,41(01):92-97.
WANG Hao-hua, MIAO Jia-you, ZHU Wan-hua, et al. Neural Network Identification Algorithm to Remove Lightning Interference from Nuclear Explosion EMP Detection[J]. Nuclear Electronics and Detection Technology, 2021,41(01):
92-97.
[13] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A—Mathematical, Physical and Engineering Sciences, 1998, 1971, 454: 903-995.
[14] Jl A , Qz A , Qw A , et al. A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors[J]. Information Sciences, 2021, 565:438-455.
[15] Jia J, Qiu W. Research on an Ensemble Classification Algorithm Based on Differential Privacy[J]. IEEE Access, 2020, PP(99):1-1.
[16] S. T. N. Ozaki and K. Horio, SVM ensemble approaches for improving texture classification performance based on complex network model with spatial information, 2018 International Workshop on Advanced Image Technology (IWAIT), 2018, pp. 1-3, doi: 10.1109/IWAIT.2018.8369742.
[17] Berrar D . Bayes' Theorem and Naive Bayes Classifier[M]// Encyclopedia of Bioinformatics and Computational Biology. 2018.
[18] Zhou H F, Zhang J W, Zhou Y Q , et al. A feature selection algorithm of decision tree based on feature weight[J]. Expert Systems with Applications, 2021, 164(4):113842.
[19] Dhananjay B , Jayaraman S . Analysis and classification of heart rate using CatBoost feature ranking model[J]. Biomedical Signal Processing and Control, 2021, 68(16):102610.
[20] Shi X , Wong Y D , Li Z F , et al. A feature learning approach based on XGBoost for driving assessment and risk prediction[J]. Accident Analysis & Prevention, 2019, 129(AUG.):170-179.
PDF(2089 KB)

Accesses

Citation

Detail

Sections
Recommended

/