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arXiv · 2603.01997

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

Hari Prasanth S. M.·Pejman Habibiroudkenar·Eerik Alamikkotervo·Dimitrios Bouzoulas·Risto Ojala
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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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Hari Prasanth S. M., Pejman Habibiroudkenar, Eerik Alamikkotervo, Dimitrios Bouzoulas, Risto Ojala. 2026-03-02. Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering. https://arxiv.org/abs/2603.01997

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