Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering

• URL: http://arxiv.org/abs/2603.01997v1
• Date: Mon, 02 Mar 2026 15:48:51 GMT
• Title: Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering
• Abstract summary: This work introduces an event-only drone forecasting method that exploits propeller-induced motion cues.<n>Propeller rotational speed are extracted directly from raw event data and fused within an RPM-aware Kalman filtering framework.
• Score: 0.5524043015533312
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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