基于自适应信号分解特征集成学习的电磁脉冲识别方法

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (8) : 275-281.

论文

程璐1,2,3，王雪晴1,2,3，刘艳3，程先友4，许鑫1,2,3，纪奕才1,2,3，方广有1,2,3

作者信息 +

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 +

文章历史 +

摘要

为准确高效地识别核爆电磁脉冲（NEMP）和闪电电磁脉冲（LEMP），提出一种基于自适应信号分解和集成学习的识别分类方法。首先，针对样本不均衡问题，利用数据扩增方法对数据集进行预处理；然后，采用希尔伯特-黄变换对 NEMP 和LEMP 分别进行自适应信号分解；其次，对分解信号提取其在时域、频域和小波域的特征；最后，对提取特征采用集成学习算法进行识别分类。实验结果表明，该方法在实测数据上的准确率能够达到99.99%以上，LEMP 信号的误报率低于万分之一。

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.

导出引用

程璐1,2,3，王雪晴1,2,3，刘艳3，程先友4，许鑫1,2,3，纪奕才1,2,3，方广有1,2,3. 基于自适应信号分解特征集成学习的电磁脉冲识别方法[J]. 振动与冲击, 2023, 42(8): 275-281

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

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