Recognition of far-region nuclear electromagnetic pulse based on Hilbert-Huang transform and least square support vector machine

Zhang Zhenchuan; Cao Baofeng; Li Peng

doi:10.11884/HPLPB202133.210059

Volume 33 Issue 7

Jul. 2021

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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

Citation:

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

Citation:

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

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Recognition of far-region nuclear electromagnetic pulse based on Hilbert-Huang transform and least square support vector machine

doi: 10.11884/HPLPB202133.210059

State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China

Received Date: 2021-02-25
Rev Recd Date: 2021-06-30

Available Online: 2021-07-09

Publish Date: 2021-07-15

Abstract

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.
- nuclear electromagnetic pulse,
- lightning electromagnetic pulse,
- Hilbert-Huang transform,
- least squares support vector machine,
- recognition

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References(18)

References

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