التفاصيل البيبلوغرافية
| العنوان: |
Environmental Interference Suppression by Hybrid Segmentation Algorithm for Open-Area Electromagnetic Capability Testing. |
| المؤلفون: |
Yang, Shun, Chen, Shuai, Zhang, Fan, Yang, Xiaqing, Shi, Jun, Zhang, Xiaoling |
| المصدر: |
Applied Sciences (2076-3417); Apr2024, Vol. 14 Issue 7, p2703, 14p |
| مصطلحات موضوعية: |
INTERFERENCE suppression, ELECTROMAGNETIC testing, IMAGE segmentation, DEEP learning, ALGORITHMS, ELECTROMAGNETIC interference, ELECTROMAGNETIC compatibility |
| مستخلص: |
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 ( E 2 I S A )) 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 E 2 I S A 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 E 2 I S A reaches up to 95% in the face of VHF (Very High Frequency) EMC testing tasks. [ABSTRACT FROM AUTHOR] |
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