Event-based Star Tracking under Spacecraft Jitter: the e-STURT Dataset
Samya Bagchi, Peter Anastasiou, Matthew Tetlow, Tat-Jun Chin, and Yasir Latif

TL;DR
This paper introduces the e-STURT dataset, a high-fidelity, event camera-based star observation dataset under controlled spacecraft jitter, enabling development of jitter estimation algorithms for space applications.
Contribution
The paper presents the first event camera-based star observation dataset under controlled jitter, including hardware emulation, systematic jitter simulation, and a baseline jitter estimation algorithm.
Findings
Dataset contains 200 sequences of star observations under jitter.
High-frequency jitter estimation algorithm demonstrated on event stream.
Dataset available publicly for research and development.
Abstract
Jitter degrades a spacecraft's fine-pointing ability required for optical communication, earth observation, and space domain awareness. Development of jitter estimation and compensation algorithms requires high-fidelity sensor observations representative of on-board jitter. In this work, we present the Event-based Star Tracking Under Jitter (e-STURT) dataset -- the first event camera based dataset of star observations under controlled jitter conditions. Specialized hardware employed for the dataset emulates an event-camera undergoing on-board jitter. While the event camera provides asynchronous, high temporal resolution star observations, systematic and repeatable jitter is introduced using a micrometer accurate piezoelectric actuator. Various jitter sources are simulated using distinct frequency bands and utilizing both axes of motion. Ground-truth jitter is captured in hardware from…
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Taxonomy
TopicsInertial Sensor and Navigation · Space Satellite Systems and Control · Ionosphere and magnetosphere dynamics
MethodsAttentive Walk-Aggregating Graph Neural Network
