Recognition of far-region nuclear electromagnetic pulse based on Hilbert-Huang transform and least square support vector machine
-
摘要: 为实现远区核爆电磁脉冲（NEMP）和闪电电磁脉冲（LEMP）的有效识别，提出一种基于希尔伯特黄变换（HHT）和最小二乘支持向量机（LSSVM）的识别算法。采用希尔伯特黄变换对远区NEMP和LEMP进行分析，利用两种信号的Hilbert谱在不同频带上分布的差异性，选择谱图中两个区域的能量占比作为信号的特征，选择LSSVM作为分类器进行分类识别。实验结果表明，采用能量占比特征可有效识别NEMP和LEMP，且综合识别率可达到98.59%。Abstract: To realize effective recognition of far-region nuclear electromagnetic pulse and lightning electromagnetic pulse, a recognition algorithm based on Hilbert-Huang transform (HHT) and least squares support vector machine was proposed. According to the difference in the distribution of the Hilbert spectrum resulted by applying HHT to NEMP and LEMP, the energy ratio of two different normalized frequency bands was selected as features of signals. In addition, least square support vector machine (LSSVM) was chosen as the classifier. A test using measured data was performed to verify the algorithm, which can effectively identify NEMP and LEMP data through the energy ratio in Hilbert spectrum with 98.59% recognition rate.
-
图 3 某次训练集下的LSSVM分类器
Figure 3. LSSVM classifier in the environment of the training data
表 1 能量占比特征值统计表
Table 1. Feature of energy ratios C1 and C2 in Hilbert spectrum
class sample size C1 C2 NEMP 32 0.918 1±0.050 9 0.007 3±0.006 8 LEMP 1068 0.550 7±0.112 7 0.175 0±0.118 7 Note: C1 and C2 values are expressed by average value ± standard deviation. 
下载: 导出CSV
表 2 10次测试的核爆、闪电和综合识别率
Table 2. NEMP, LEMP and total recognition accuracy in ten tests
No. accuracy/% NEMP LEMP total 1 100 98.57 98.59 2 100 98.47 98.49 3 100 98.57 98.59 4 100 98.98 98.99 5 100 98.67 98.69 6 100 98.36 98.38 7 100 98.27 98.28 8 100 98.57 98.59 9 100 98.78 98.79 10 100 98.47 98.49
下载: 导出CSV
-
[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(3):109-119. (Feng Yimin. The characteristics and mechanism of high altitude nuclear explosion electromagnetic pulse[J]. Journal of Military Communications Technology, 1984(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] 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 [5] 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 [6] 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, 24(2):143-146. (Li Xihai, Liu Zhigang, Wu Hongxia, 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 doi: 10.3969/j.issn.0258-0934.2004.02.009 [7] 祁树锋, 曾泰, 李夕海. 基于小波包分形分析的核爆与雷电电磁脉冲识别[M]//国家安全地球物理丛书(八)——遥感地球物理与国家安全. 2012: 348-352.Qi Shufeng, Zeng Tai, Li Xihai, et al. Discrimination of nuclear explosion and lightning electromagnetic pulse using wavelet packet fractal analysis[C]//National Security Geophysics Series (8)— Remote Sensing Geophysics and National Security. 2012: 348-352 [8] 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. (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 doi: 10.3969/j.issn.1000-3835.2013.07.002 [9] 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):259-2263. (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): 259-2263 [10] 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. Applications of wavelet transform in the recognition of NEMP and LEMP signals[J]. Journal of Electronics, 1999, 21(5): 710-712 [11] Li Peng, Zheng Yi, Han Chao, et al. Lightning and nuclear explosion pattern recognition from optical and electromagnetic data[C]//11th International Conference on Fuzzy Systems and Knowledge Discovery. 2014: 485-489. [12] 祁树锋, 李夕海, 韩绍卿, 等. 基于时频图像分析的核爆与雷电电磁脉冲识别[J]. 强激光与粒子束, 2013, 25(2):522-526. (Qi Shufeng, Li Xihai, Han Shaoqing, et al. Discrimination of nuclear explosion and lightning electromagnetic pulse using time-frequency image analysis[J]. High Power Laser and Particle Beams, 2013, 25(2): 522-526 doi: 10.3788/HPLPB20132502.0522 [13] 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 [14] 陈祝东. 远区核爆电磁脉冲[M]. 北京: 解放军出版社, 1985.Chen Zhudong. Nuclear explosion electromagnetic pulses in distance area[M]. Beijing: The People’s Liberation Army Press, 1985 [15] 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. [16] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 12(6): 293-300. [17] 尹嘉鹏. 支持向量机核函数及关键参数选择研究[D]. 哈尔滨: 哈尔滨工业大学, 2016: 40-45.Yin Jiapeng. Research on selection of kernel functions and key parameters in support vector machine[D]. Harbin: Harbin Institute of Technology, 2016: 40-45 [18] 郑毅, 李鹏, 耿娜, 等. 核爆炸监测技术概论[M]. 北京: 国防工业出版社, 2019.Zheng Yi, Li Peng, Geng Na, et al. Introduction to nuclear explosion monitoring technology[M]. Beijing: National Defense Industry Press, 2019 -
点击查看大图
图(3) / 表(2)
计量
- 文章访问数: 993
- HTML全文浏览量: 273
- PDF下载量: 51
- 被引次数: 0
