Predicting EMI in UAVs using characteristic mode analysis: a case study of the DJI Phantom 4

Unmanned Aerial Vehicles (UAVs) are increasingly vital across various sectors, including aerial photography, agriculture, infrastructure inspection, and search and rescue operations. Beyond these applications, UAVs represent a practical and efficient embodiment of the IoT, offering a viable alternative to traditional, labor-intensive methods for inspecting critical infrastructure, such as power transmission lines, base stations, and radio transmission towers. By leveraging advanced technologies, UAVs significantly reduce the need for human resources in routine maintenance tasks, thereby enhancing the reliability and safety of essential infrastructure systems¹.

Despite these advantages, UAVs face significant operational challenges, particularly their susceptibility to EMI². According to the DJI Phantom 4 user manual³, it is crucial to avoid operating near high-voltage power lines and areas with elevated electromagnetic activity to minimize the risk of interference. Modeling coupled currents in UAV wiring and electronics due to EMI presents significant challenges. Studies typically focus on a simplified UAV model, conducting numerous simulations and measurements to assess the induced currents under various conditions and environments^4,5,6. However, these approaches do not capture the full electromagnetic behavior of UAVs in real-world scenarios, where multiple subsystems and their interactions play a critical role in EMI susceptibility.

The coupled currents within a UAV can vary significantly based on its orientation relative to the incident electromagnetic field’s direction, polarization, and frequency^4,6.Current methodologies for Electromagnetic Compatibility (EMC) testing rely heavily on extensive experimental validation, which is often time-consuming and resource-intensive⁷. CMA offers an efficient alternative for predicting and quantifying EMI effects by identifying the dominant resonant modes that contribute to electromagnetic coupling⁸. By applying CMA, researchers can more accurately target their experimental measurements, reducing the need for exhaustive testing while still ensuring that all critical EMI interactions are thoroughly analyzed.

CMA has been instrumental in optimizing antenna designs, providing deep insights into the modal behavior of electromagnetic structures. This method enables systematic enhancements to antenna performance parameters, allowing for efficient adjustments to dimensions and structures based on modal analysis^{9,10,11,12,13,14,15}. The original concept of characteristic modes was first introduced in 1965 to analyze electromagnetic scattering, treating obstacles as parasitically excited antennas with virtual terminals. This approach enables a deeper understanding of resonance phenomena, as characteristic modes are inherently linked to an object’s shape and influence field interactions¹⁶. This concept later extended using the Method of Moments (MoM), which facilitated the practical computation of CMA for complex electromagnetic structures^8,17. Since its introduction, CMA has become a powerful tool for solving complex electromagnetic problems, evolving beyond its initial focus on perfect electric conductors to incorporate dielectric bodies^18,19. These advancements have significantly expanded its utility in areas such as reconfigurable antennas¹⁵ achieved frequency tunability, while improved MIMO performance²⁰ by reducing envelope correlation. CM-based loading strategies enhanced bandwidth in^21,22 demonstrated multi-resonant small antennas. Mutual coupling mitigation in MIMO systems was addressed in²³, with²⁴ further optimizing wideband performance. This study leverages CMA to analyze electromagnetic coupling and interference in UAV structures, providing deeper insights into EMI effects across all UAV’s subsystems.

Previous studies^{25,26,27,28,29} have often relied on simplified UAV models comprising four wires attached to a square metallic patch that do not account for the full range of subsystems comprising a UAV. Such models, while useful for certain analyses, fail to capture the complexity and interactions between various components that significantly influence a UAV’s electromagnetic behavior.

This study extends the application of CMA to UAV EMI analysis by considering all major subsystems of the DJI Phantom 4, including the GPS module, motors, ESC boards, antennas, and other electronic components. Unlike previous research, which focuses on single-component interactions, this work evaluates the cumulative effects of EMI on an entire UAV system. Additionally, while prior studies applied CMA to UAVs in a narrowband approach, this work extends the analysis over a wide frequency range, demonstrating that UAVs are more susceptible to interference at higher frequencies. Another novel contribution is the statistical analysis performed. The analysis is structured in three stages: the first stage examines the UAV’s primary structure, the second stage incorporates key electronic components, and the final stage includes all subsystems to provide a complete electromagnetic representation.

CMA is applied to analyze and mitigate the susceptibility of quadcopter UAVs to EMI. Initially, the fundamental modes of the proposed UAV model are determined and analyzed. By understanding these modes, the behavior of coupled currents induced at various locations on the UAV structure, when subjected to plane wave excitations, is predicted across a range of frequencies and incident directions. The calculated modes serve as critical reference points for understanding how electromagnetic fields interact with the UAV. By identifying the specific modes that resonate at given frequencies and directions, it becomes possible to predict the resulting electromagnetic coupling at different points on the UAV.

This approach allows for a more accurate identification of EMI-vulnerable regions and offers valuable insights into potential vulnerabilities mitigation strategies. By integrating CMA simulations with experimental validation, this research bridges the gap between theoretical predictions and real-world EMI effects on UAVs. The findings highlight the importance of shielding strategies, particularly for power supply cables, and provide recommendations for improving UAV electromagnetic resilience. This study contributes to the broader field of UAV electromagnetic compatibility and serves as a foundation for future investigations into EMI mitigation techniques.

The structure of the study is outlined as follows: Section II presents the UAV subsystem analysis using CMA, detailing the three-stage modeling approach. Section III evaluates the UAV’s electromagnetic behavior through CMA, identifying resonant modes. Section IV validates the CMA results through full-wave simulations. Section V analyzes UAV subsystem sensitivity using modal significance and surface current distribution. Section VI describes the experimental validation of UAV electromagnetic susceptibility. Finally, Section VII summarizes the conclusions and discusses implications for UAV electromagnetic resilience.