Environmental Interference Suppression by Hybrid Segmentation Algorithm for Open-Area Electromagnetic Capability Testing

Author:

Yang Shun12,Chen Shuai1,Zhang Fan1ORCID,Yang Xiaqing3ORCID,Shi Jun1,Zhang Xiaoling1

Affiliation:

1. School of Information and Communication Engineering, Qingshuihe Campus, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China

2. AVIC Chengdu Aircraft Industrial (Group) Co., Ltd., Chengdu 610041, China

3. School of Resources and Environment, Qingshuihe Campus, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China

Abstract

Compared with electromagnetic compatibility (EMC) testing in anechoic rooms, open-area EMC testing takes advantage of in situ and engine running status measurement but suffers from non-negligible external electromagnetic interference. This paper proposes a novel environmental interference suppression method (named the EMC environmental interference suppression algorithm (E2ISA)) that separates signals from backgrounds via image segmentation and recognizes the near–far site signal via a group of time-varying features based on the difference in the near-site EM radiative characteristic. We find that the proposed E2ISA method, which combines the deep learning segmentation network with the classical recognition methods, is able to suppress environmental interference signals accurately. The experiment results show that the accuracy of E2ISA reaches up to 95% in the face of VHF (Very High Frequency) EMC testing tasks.

Funder

National Key R&D Program of China

Publisher

MDPI AG

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