EBS-EKF: Accurate and High Frequency Event-based Star Tracking

2025-03-25

AI Summary

To address the dual requirements of high accuracy and low latency in star trackers, this paper proposes a novel event-driven star tracking method. The approach introduces a physics-inspired event-generation circuit model and integrates it with an Extended Kalman Filter (EKF) to enable real-time star detection, correspondence matching, and attitude state estimation. We present the first quantitative validation of event-driven star identification on real nocturnal sky data and release the first synchronized event-APS star image benchmark dataset. Compared to space-grade active pixel sensor (APS) star trackers, our method achieves an order-of-magnitude improvement in pointing accuracy, supports higher update rates, and exhibits significantly enhanced tolerance to angular velocity. Experimental results demonstrate that the system attains space-readiness-level accuracy under realistic operational conditions.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Develops accurate event-based star tracking algorithm
• Improves signal modeling and state estimation
• Compares performance with conventional APS trackers

Innovation

• Event-based sensors for star tracking
• Extended Kalman filter for state estimation
• Improved signal modeling and accuracy