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Classification of Electromagnetic Radiation Source Models Based on Directivity with the Method of Machine Learning
Zhuo LIU Dan SHI Yougang GAO Junjian BI Zhiliang TAN Jingjing SHI
IEICE TRANSACTIONS on Communications
Publication Date: 2015/07/01
Online ISSN: 1745-1345
Type of Manuscript: Special Section PAPER (Special Section on Electromagnetic Compatibility Technology in Conjunction with Main Topics of EMC'14/Tokyo)
classification, directivity, radiation pattern, machine learning, cube receiving array,
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This paper presents a new way to classify different radiation sources by the parameter of directivity, which is a characteristic parameter of electromagnetic radiation sources. The parameter can be determined from measurements of the electric field intensity radiating in all directions in space. We develop three basic antenna models, which are for 3GHz operation, and set 125,000 groups of cube receiving arrays along the main lobe of their radiation patterns to receive the data of far field electric intensity in groups. Then the Back Propagation (BP) neural network and the Support Vector Machine (SVM) method are adopted to analyze training data set, and build and test the classification model. Owing to the powerful nonlinear simulation ability, the SVM method offers higher classification accuracy than the BP neural network in noise environment. At last, the classification model is comprehensively evaluated in three aspects, which are capability of noise immunity, F1 measure and the normalization method.