EBS-EKF: Accurate and High Frequency Event-based Star Tracking
This work addresses star tracking for space applications, offering a more accurate and efficient solution, though it builds on existing sensor technology and filtering techniques.
The paper tackles the problem of star tracking using event-based sensors by proposing a novel algorithm that combines circuit analysis with an extended Kalman filter, demonstrating an order-of-magnitude improvement in accuracy and higher update frequency compared to conventional methods on real night sky data.
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.