Remote monitoring of drones has become a global objective due to emerging applications in national security and managing aerial delivery traffic. Despite their relatively small size, drones can carry significant payloads, which require monitoring, especially in cases of unauthorized transportation of dangerous goods. A drone's flight dynamics heavily depend on outdoor wind conditions and the carry-on weight, which affect the tilt angle of a drone's body and the rotation velocity of the blades. A surveillance radar can capture both effects, provided a sufficient signal-to-noise ratio for the received echoes and an adjusted postprocessing detection algorithm. Here, we conduct a systematic study to demonstrate that micro-Doppler analysis enables the disentanglement of the impacts of wind and weight on a hovering drone. The physics behind the effect is related to the flight controller, as the way the drone counteracts weight and wind differs. When the payload is balanced, it imposes an additional load symmetrically on all four rotors, causing them to rotate faster, thereby generating a blade-related micro-Doppler shift at a higher frequency. However, the impact of the wind is different. The wind attempts to displace the drone, and to counteract this, the drone tilts to the side. As a result, the forward and rear rotors rotate at different velocities to maintain the tilt angle of the drone body relative to the airflow direction. This causes the splitting in the micro-Doppler spectra. By performing a set of experiments in a controlled environment, specifically, an anechoic chamber for electromagnetic isolation and a wind tunnel for imposing deterministic wind conditions, we demonstrate that both wind and payload details can be extracted using a simple deterministic algorithm based on branching in the micro-Doppler spectra.

翻译：由于在国家安全和空中物流管理中的新兴应用，无人机远程监测已成为全球性目标。尽管无人机体积相对较小，但其可携带显著的有效载荷，尤其在非法运输危险品的情况下需予以监控。无人机的飞行动力学高度依赖于外部风况与载重，二者共同影响无人机机身的倾斜角及旋翼转速。在回波信噪比充足且检测后处理算法适配的条件下，监视雷达可同时捕获这两种效应。本文通过系统性研究证明，微多普勒分析能够解耦风力和载重对悬停无人机的影响。其背后的物理机制与飞行控制器相关，因为无人机对抗载重与风力的方式存在差异。当载荷平衡时，其对称地施加于四个旋翼的额外负载会导致旋翼加速旋转，从而在更高频率处产生与旋翼相关的微多普勒频移。然而，风力的影响则不同：风试图使无人机位移，为抵消此效应，无人机会向侧向倾斜。这导致前后旋翼以不同转速运转，以维持机身相对于气流方向的倾斜角，进而引起微多普勒谱的分裂。通过在受控环境（具体为电磁隔离的消声室和可施加确定性风况的风洞）中进行一系列实验，我们证明基于微多普勒谱分叉的简单确定性算法可同时提取风力与载荷的详细信息。