Predicting EMI in UAVs using characteristic mode analysis: a case study of the DJI Phantom 4
Abstract Unmanned aerial vehicles (UAVs) are increasingly utilized across various sectors, particularly in Internet of Things (IoT) applications. However, their susceptibility to electromagnetic interference (EMI) poses significant challenges to operational performance. This study assesses the susce...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-94415-1 |
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| Summary: | Abstract Unmanned aerial vehicles (UAVs) are increasingly utilized across various sectors, particularly in Internet of Things (IoT) applications. However, their susceptibility to electromagnetic interference (EMI) poses significant challenges to operational performance. This study assesses the susceptibility of a DJI Phantom 4 UAV to EMI using Characteristic Mode Analysis (CMA) and experimental validation. Unlike prior studies that focus on simplified UAV models, this research incorporates all subsystems to enhance CMA accuracy. By extending the analysis over a wide frequency range, it identifies components with high induced currents and demonstrates that UAVs are more susceptible to interference at higher frequencies. Experimental validation further confirms the EMI-induced failure risks of these components, highlighting the importance of structural details in electromagnetic assessments. Subsequent experimental testing assessed which of these components were most vulnerable to EMI-induced damage. Findings reveal that long power supply wires, particularly those connecting motors to ESC boards, exhibit the highest susceptibility. The results underscore the necessity of shielding power supply cables, akin to coaxial cable design, to enhance the UAV’s electromagnetic resilience. These insights provide applicability across a range of UAV designs, facilitating improved performance in electromagnetically dense environments. |
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| ISSN: | 2045-2322 |