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

arXiv: https://arxiv.org/abs/2603.01997 · HTML: https://mlsanity.com/paper/2603.01997/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark](https://mlsanity.com/paper/2103.13933/) (2021)
- [EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following](https://mlsanity.com/paper/2106.15045/) (2021)
- [FRED: The Florence RGB-Event Drone Dataset](https://mlsanity.com/paper/2506.05163/) (2025)
- [Neuromorphic Drone Detection: an Event-RGB Multimodal Approach](https://mlsanity.com/paper/2409.16099/) (2024)
- [A Unified Framework for Joint Mobility Prediction and Object Profiling of Drones in UAV Networks](https://mlsanity.com/paper/1808.00058/) (2018)
- [High Speed Rotation Estimation with Dynamic Vision Sensors](https://mlsanity.com/paper/2209.02205/) (2022)
- [Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction](https://mlsanity.com/paper/2107.00422/) (2021)
- [Drone Detection Using a Low-Power Neuromorphic Virtual Tripwire](https://mlsanity.com/paper/2509.12997/) (2025)

## Built on by (most cited first, 1 of 1)

- [Residual Kalman Dynamics for Event-Based UAV Forecasting](https://mlsanity.com/paper/2609.00839/) (2026)

## Abstract (arXiv)

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.