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A Novel Classification Method Based on Adaboost for Electromagnetic Emission

机译：基于Adaboost的电磁辐射分类新方法。

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摘要

Abundant characteristics information of equipment or systems could be obtained from electromagnetic emission data. In this paper, those typical characteristics, like harmonics, damped oscillations, of electromagnetic emission are classified via the adaptive boosting (Adaboost) algorithm and they are validated through measurement results. Based on the "basic emission waveform theory", three types of the basic fundamental elements, characteristics-harmonic, narrowband and envelope-of complex emission in frequency domain, are considered in our proposed method. By taking weights combination patterns to effectively improve the classification performance of a single classifier, quite high classification accuracy could be achieved by Adaboost algorithm in our simulations. 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 with decision tree weak-classifier used to classify typical characteristics of electromagnetic emission is the most accurate. At the same time, it is very effective to process the measured data. Only through the classification of multiple emission signals can identification and positioning of electromagnetic interference sources further.
机译：设备或系统的丰富特性信息可以从电磁辐射数据中获得。在本文中，通过自适应增强（Adaboost）算法对电磁辐射的那些典型特征（如谐波，阻尼振荡）进行了分类，并通过测量结果对其进行了验证。基于“基本发射波形理论”，本文提出的方法考虑了三种基本基本元素，即频域复杂辐射的特征谐波，窄带和包络。通过采用权重组合模式来有效提高单个分类器的分类性能，在我们的仿真中，Adaboost算法可以实现很高的分类精度。在我们的研究中，使用具有13个决策树弱分类器的Adaboost可以获得三种类型特征的100％精度分类精度。与其他分类方法相比，带有决策树弱分类器的Adaboost算法用于对电磁辐射的典型特征进行分类是最准确的。同时，对测量数据进行处理非常有效。只有通过对多个发射信号进行分类，才能进一步识别和定位电磁干扰源。

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