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[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()
基于改进的多因素SVM算法的太阳射电暴强度检测
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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
基于改进的多因素SVM算法的太阳射电暴强度检测
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
罗铱镅1;  祝雪芬1;  林梦颖1;  杨帆1;  涂刚毅2
1东南大学仪器科学与工程学院, 南京210096; 2南京信息工程大学电子与信息工程学院, 南京210044
Keywords:
global navigation satellite system;  solar radio burst;  modified multifactor SVM algorithm;  detection accuracy
全球导航卫星系统;  太阳射电爆发;  改进的多因素支持向量机;  检测精度
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.
为实现对太阳射电暴(SRBs)强度的自动检测, 提出了一种基于改进的多因素支持向量机(SVM)的SRBs强度检测方法.首先, 分析SRBs对全球导航卫星系统(GNSS)信号的影响, 提取能够反映接收站点SRBs强度的特征向量.根据太阳射电流量对SRBs强度进行分类标签, 不同标签对应不同SRBs强度类型, 训练样本由特征向量及其标签组成.其次, 将具有不同标签的训练样本输入SVM分类器, 进行一对一训练, 得到最优分类模型.最后, 将最优分类模型合成为改进的多因素SVM分类器, 用于自动识别更新后的SRBs样本.实验结果表明, 对于历史SRBs事件, 当太阳入射角大于20°时, SRBs强度检测的平均准确率在90%以上.该算法综合考虑了多种因素, 可进行SRBs强度的自动检测, 与其他方法相比, 该方法不依赖射电望远镜, 节约了成本且检测精度更高.

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.
Last Update: 2022-03-20