Radiation Emitter Classification and Identification Approach Based on Radiation Emission Components †

Featured Application: In the area of electromagnetic compatibility (EMC), it is necessary to locate the source of interference according to the exceeded radiation emission in EMC testing, which can guide the EMC rectification strategy. However, due to the abundant noise level and complex regularities, it is difficult to identify the emission source by using the testing data directly. The measurement and data analysis of radiation emitters mainly depend on manual work, which has low efficiency and relies deeply on personal experience for EMC rectification. In this paper, a systematic radiation emitter classification and identification approach, based on radiation emission components, was proposed. The equipment type and its operating status can be identified by analyzing the equipment’s unintended radiated emissions based on radiation emission components. This can offer an improvement upon the existing interference source location methods in EMC tests. The proposed method can significantly improve the reliability and engineering applicability of the identification and location of the interference source in EMC tests, and it can provide fast technical support for EMC design. An electronic system generates a large number of intended or unintended electromagnetic radiated emissions in its operating state, which can lead to potential electromagnetic compatibility (EMC) problems. To avoid the impact of these electromagnetic radiation emissions on the surrounding electronic equipment or systems, it is necessary to classify and identify these radiation emitters. In this paper, we proposed a systematic approach for radiation emitter classification and identification based on radiation emission components. Inspired by the basic emission waveform theory (BEWT), the radiation emission data of electronic systems are decomposed into three kinds of radiation emission components, and the different groups of radiation emission data can be classified or identified according to the similarity of these radiation emission components. The radiation emission (RE) spectral data of several kinds of electronic equipment, such as laptops and digital cameras, were used to verify the proposed method. The classification and identification accuracy for data corresponding to different kinds of radiation emission spectra was about 99%, which confirmed the effectiveness of this method.