[1] Luo Yimei, Zhu Xuefen, Lin Mengying, Yang Fan, et al. Detection of solar radio burst intensitybased on a modified multifactor SVM algorithm [J]. Journal of Southeast University (English Edition), 2022, 38 (1): 20-26. [doi:10.3969/j.issn.1003-7985.2022.01.004]

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Detection of solar radio burst intensitybased on a modified multifactor SVM algorithm()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

38

Issue:

2022 1

Page:

20-26

Research Field:

Electromagnetic Field and Microwave Technology

Publishing date:

2022-03-20

Info

Title:

Detection of solar radio burst intensitybased on a modified multifactor SVM algorithm

Author(s):

Luo Yimei¹;  Zhu Xuefen¹;  Lin Mengying¹;  Yang Fan¹;  Tu Gangyi²

¹School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
²School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Keywords:

global navigation satellite system;  solar radio burst;  modified multifactor SVM algorithm;  detection accuracy

PACS:

TN967.1

DOI:

10.3969/j.issn.1003-7985.2022.01.004

Abstract:

To realize the automatic detection of solar radio burst(SRB)intensity, detection based on a modified multifactor support vector machine(SVM)algorithm is proposed. First, the influence of SRB on global navigation satellite system(GNSS)signals is analyzed. Feature vectors, which can reflect the SRB intensity of stations, are also extracted. SRB intensity is classified according to the solar radio flux, and different class labels correspond to different SRB intensity types. The training samples are composed of feature vectors and their corresponding class labels. Second, training samples are input into SVM classifiers to one-against-one training to obtain the optimal classification models. Finally, the optimal classification model is synthesized into a modified multifactor SVM classifier, which is used to automatically detect the SRB intensity of new data. Experimental results indicate that for historical SRB events, the average accuracy of SRB intensity detection is greater than 90% when the solar incident angle is higher than 20°. Compared with other methods, the proposed method considers many factors with higher accuracy and does not rely on radio telescopes, thereby saving cost.

References:

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Memo

Memo:

Biographies: Luo Yimei(1997—), female, graduate; Zhu Xuefen(corresponding author), female, doctor, associate professor, zhuxuefen@seu.edu.cn.
Foundation items: The National Key Research and Development Plan of China(No. 2018YFB0505103), the National Natural Science Foundation of China(No. 61873064).
Citation: Luo Yimei, Zhu Xuefen, Lin Mengying, et al.Detection of solar radio burst intensity based on a modified multifactor SVM algorithm[J].Journal of Southeast University(English Edition), 2022, 38(1):20-26.DOI:10.3969/j.issn.1003-7985.2022.01.004.

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