Spectrum Data Feature Analysis Based on Support Vector Machine Method

被引：0
|
作者：
Wu, Jiayi [1 ]
Cui, Shuo [1 ]
Su, Donglin [1 ]
机构：
[1] Beihang Univ, Sch Elect & Informat Engn, 37 Xueyuan Rd, Beijing 100191, Peoples R China
来源：
2017 IEEE SIXTH ASIA-PACIFIC CONFERENCE ON ANTENNAS AND PROPAGATION (APCAP) | 2017年
基金：
中国国家自然科学基金;
关键词：
Electromagnetic emission; svm; Spectrum; Electromagnetic compatibilitys;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Electromagnetic emission characteristics can be detected from the work-state equipment or system for effective electromagnetic emission analysis. Regardless of how complex the electromagnetic emission spectrum is, the electromagnetic emission characteristics of the spectral data can be obtained by analysis. Analysis of harmonic components, narrowband components and broadband components is very important in identifying electromagnetic emission characteristics and sources. Support Vector Machine(SVM), which has many unique advantages in solving small sample, nonlinear and high dimensional pattern recognition questions, can also be applied to machine learning problems such as function fitting. In this paper, the SVM method is used to analyze the characteristics of electromagnetic emission spectrum data. The analysis targets include harmonic interference, narrowband interference and the broadband interference. After applying our method, the recognition accuracy is also determined correspondingly. The feature classification obtained above can be used to guide the equipment's/system's electromagnetic compatibility designs and problem-shooting processes.
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收藏
页数：3
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