Recognition of far-region nuclear electromagnetic pulse based on wavelet fractal technique

Zhang Zhenchuan; Cao Baofeng; Li Peng; Yin Hao; Wu Huichun

doi:10.11884/HPLPB202234.210375

Volume 34 Issue 6

Apr. 2022

Turn off MathJax

Article Contents

Article Navigation > High Power Laser and Particle Beams > 2022 > 34(6): 066002

Zhang Zhenchuan, Cao Baofeng, Li Peng, et al. Recognition of far-region nuclear electromagnetic pulse based on wavelet fractal technique[J]. High Power Laser and Particle Beams, 2022, 34: 066002. doi: 10.11884/HPLPB202234.210375

Citation:

Zhang Zhenchuan, Cao Baofeng, Li Peng, et al. Recognition of far-region nuclear electromagnetic pulse based on wavelet fractal technique[J]. High Power Laser and Particle Beams, 2022, 34: 066002. doi: 10.11884/HPLPB202234.210375

Citation:

PDF( 939 KB)

Recognition of far-region nuclear electromagnetic pulse based on wavelet fractal technique

doi: 10.11884/HPLPB202234.210375

1.
State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China
2.
Physics Department of Zhejiang University, Hangzhou 310058, China

Received Date: 2021-08-27
Rev Recd Date: 2022-02-07

Available Online: 2022-03-02

Publish Date: 2022-06-15

Abstract

Abstract

Aiming at the difficulty to identify far-region nuclear and lightning electromagnetic pulse correctly, a recognition method based on wavelet fractal technique was proposed. First, the signals of nuclear electromagnetic pulse (NEMP) and lightning electromagnetic pulse (LEMP), preprocessed by interpolation and normalization, were decomposed by wavelet packet at level two. Then, fractal dimensions of reconstructed signal from wavelet packet coefficients were calculated to form eigenvectors of NEMP and LEMP. Least squares support vector machine was chosen as the classifier and the model parameters were obtained by the five-fold cross validation. Finally, the eigenvectors were input into least squares support vector machine (LSSVM) for training and testing. The experimental results show that the combination of wavelet fractal technique and LSSVM performs well in recognition of NEMP and LEMP; the average recognition rate is more than 99%.
- nuclear electromagnetic pulse,
- lightning electromagnetic pulse,
- wavelet fractal,
- least squares support vector machine,
- recognition

FullText(HTML)

References(14)

References

[1]	谢彦召, 王赞基, 王群书, 等. 高空核爆电磁脉冲波形标准及特征分析[J]. 强激光与粒子束, 2003, 15(8):781-787. (Xie Yanzhao, Wang Zanji, Wang Qunshu, et al. High altitude nuclear electromagnetic pulse waveform standards: a review[J]. High Power Laser and Particle Beams, 2003, 15(8): 781-787
[2]	冯一民. 高空核爆电磁脉冲的特点及其机理[J]. 军事通信技术, 1984, 5(3):109-119. (Feng Yimin. The characteristics and mechanism of high altitude nuclear explosion electromagnetic pulse[J]. Journal of Military Communications Technology, 1984, 5(3): 109-119
[3]	吴江峰, 罗霞, 王锋. 利用核电磁脉冲进行远区核爆探测方法研究[C]//第十三届全国核电子学与核探测技术学术年会论文集(下册). 2006: 123-125 Wu Jiangfeng, Luo Xia, Wang Feng. Discussing on far-region nuclear burst detection by NEMP[C]//Proceedings of the 13th National Annual Conference on Nuclear Electronics and Nuclear Detection Technology (Volume Two). 2006: 123-125
[4]	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(8):159-164. (Wang Tao, Chen Fugui, 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(8): 159-164
[5]	李鹏, 宋立军, 韩超, 等. 基于AR模型与神经网络的核爆与闪电电磁脉冲信号识别[J]. 强激光与粒子束, 2010, 22(12):3052-3056. (Li Peng, Song Lijun, 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 doi: 10.3788/HPLPB20102212.3052
[6]	庞新良, 李鹏, 范江兵, 等. 小波计盒分形维数核爆电磁脉冲信号模糊识别[J]. 强激光与粒子束, 2012, 24(9):2259-2263. (Pang Xinliang, Li Peng, Fan Jiangbing, et al. Fuzzy identification of NEMP signal based on wavelet and box-counting dimension eigenvalue[J]. High Power Laser and Particle Beams, 2012, 24(9): 2259-2263 doi: 10.3788/HPLPB20122409.2259
[7]	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(7):8-10,16. (Qi Shufeng, Li Xihai, Han Shaoqing, et al. Discrimination of nuclear explosion and lightning electromagnetic pulses using multi-fractal analysis[J]. Journal of Vibration and Shock, 2013, 32(7): 8-10,16 doi: 10.3969/j.issn.1000-3835.2013.07.002
[8]	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]. 电子科学学刊, 1999, 21(5):710-712. (Zhang Xurong, Zhang Miaolan, Liu Xinzhong. Studies of recognition methods of nuclear and lightning impulse signals with applications of wavelet transform[J]. Journal of Electronics, 1999, 21(5): 710-712
[9]	Li Peng, Zheng Yi, Han Chao, et al. Lightning and nuclear explosion pattern recognition from optical and electromagnetic data[C]//2014 11th International Conference on Fuzzy Systems and Knowledge Discovery. 2014: 485-489.
[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(3):163-166. (Qi Shufeng, Li Xihai, Han Shaoqing, 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(3): 163-166 doi: 10.3969/j.issn.1000-3835.2013.03.032
[11]	陈祝东. 远区核爆电磁脉冲[M]. 北京: 解放军出版社, 1985: 6 Chen Zhudong. Nuclear explosion electromagnetic pulses in distance area[M]. Beijing: The People’s Liberation Army Press, 1985: 6
[12]	罗毅, 甄立敬. 基于小波包与倒频谱分析的风电机组齿轮箱齿轮裂纹诊断方法[J]. 振动与冲击, 2015, 34(3):210-214. (Luo Yi, Zhen Lijing. Diagnosis method of turbine gearbox gearcrack based on wavelet packet and cepstrum analysis[J]. Journal of Vibration and Shock, 2015, 34(3): 210-214
[13]	JI Wei, Chen Guangyu, Xu Bo, et al. Recognition method of green pepper in greenhouse based on least-squares support vector machine optimized by the improved particle swarm optimization[J]. IEEE Access, 2019, 7: 119742-119754. doi: 10.1109/ACCESS.2019.2937326
[14]	张震川, 曹保锋, 李鹏. 基于HHT与LSSVM的远区核爆电磁脉冲识别[J]. 强激光与粒子束, 2021, 33:076003. (Zhang Zhenchuan, Cao Baofeng, Li Peng. Recognition of far-region nuclear electromagnetic pulse based on Hilbert-Huang transform and least square support vector machine[J]. High Power Laser and Particle Beams, 2021, 33: 076003 doi: 10.11884/HPLPB202133.210059