Event cameras provide high-temporal-resolution visual sensing that is well suited for observing fast-moving aerial objects; however, their use for drone trajectory prediction remains limited. This work introduces an event-only drone forecasting method that exploits propeller-induced motion cues. Propeller rotational speed are extracted directly from raw event data and fused within an RPM-aware Kalman filtering framework. Evaluations on the FRED dataset show that the proposed method outperforms learning-based approaches and vanilla kalman filter in terms of average distance error and final distance error at 0.4s and 0.8s forecasting horizons. The results demonstrate robust and accurate short- and medium-horizon trajectory forecasting without reliance on RGB imagery or training data.

翻译：事件相机提供的高时间分辨率视觉感知非常适用于观测高速运动的空中目标，然而其在无人机轨迹预测中的应用仍然有限。本文提出一种纯事件驱动的无人机预测方法，该方法利用螺旋桨诱导的运动线索。螺旋桨转速直接从原始事件数据中提取，并在RPM感知的卡尔曼滤波框架中进行融合。在FRED数据集上的评估表明，所提方法在0.4秒和0.8秒预测时域的平均距离误差和最终距离误差方面均优于基于学习的方法及基础卡尔曼滤波器。结果表明，该方法无需依赖RGB图像或训练数据，即可实现鲁棒且准确的短时及中时域轨迹预测。