A Novel Classification Method Based on Adaboost for Electromagnetic Emission Characteristics
Jing Xu Nie, Shunchuan Yang, Qiang Ren, Donglin Su · 2018 International Applied Computational Electromagnetics Society Symposium - China (ACES) · 2018
Abundant characteristics information of equipment or systems could be obtained from electromagnetic emission data. In this paper, those characteristics of electromagnetic emission are analyzed via the adaptive boosting (Adaboost) algorithm. Based on the “basic emission waveform theory”, four types of basic fundamental elements, characteristics-harmonic, narrowband and envelope-of complex emission in frequency domain, could be extracted. For taking weights combination patterns to effectively improve the classification performance of a single classifier, high classification accuracy could be achieved by Adaboost algorithm. In our study, 100% precision classification accuracy of three types of characteristics could be obtained using Adaboost with 13 decision tree weak-classifiers. Compared with other classification methods, the Adaboost algorithm used in this paper is the most accurate. The outcomes of this research can be used as a guide in the electromagnetic compatibility design and rectification.