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
1.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China;
2.Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Sciences, Beijing 100190, China;
3.University of Chinese Academy of Sciences, Beijing 100190, China;
4.State Key Laboratory of Protection for Civilian, Research Institute of Chemical Defense, Beijing 102205, China
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.
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. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(8): 275-281.
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