Event-based sensors (EBS) are a promising new technology for star tracking due to their low latency and power efficiency, but prior work has thus far been evaluated exclusively in simulation with simplified signal models. We propose a novel algorithm for event-based star tracking, grounded in an analysis of the EBS circuit and an extended Kalman filter (EKF). We quantitatively evaluate our method using real night sky data, comparing its results with those from a space-ready active-pixel sensor (APS) star tracker.
We demonstrate that our method is an order-of-magnitude more accurate than existing methods due to improved signal modeling and state estimation, while providing more frequent updates and greater motion tolerance than conventional APS trackers. We provide all code and the first dataset of events synchronized with APS solutions.
Overview of related works. Color indicates high (green), medium (yellow) and low (red) performance. “s” denotes seconds. Compared to previous works, ours is the only one to 1) be evaluated on night sky data that is open source, 2) incorporate 3D state estimation, 3) require absolute orientations updates only on initialization, and 4) have subpixel centroiding precision
Metadata and tracking results for our real world dataset. Metrics are reported as (across, about) mean squared difference in arcseconds. Colors rank methods with the first (green), second (yellow), or third (red) lowest across and about means. Across all of the data runs, EBS-EKF is a magnitude or more accurate than the next closest algorithm. The intensity-dependent offset usually improves the accuracy due to the higher centroiding precision.
@article{reed2025ebs,
title={EBS-EKF: Accurate and High Frequency Event-based Star Tracking},
author={Reed, Albert W and Hashemi, Connor and Melamed, Dennis and Menon, Nitesh and Hirakawa, Keigo and McCloskey, Scott},
journal={arXiv preprint arXiv:2503.20101},
year={2025}
}