基于事件传感器的高精度高频星体追踪算法
EBS-EKF: Accurate and High Frequency Event-based Star Tracking
- 结合电路分析与扩展卡尔曼滤波，改进信号建模与状态估计
- 实测数据表明精度比现有方法提升一个数量级
- 适合需要高频更新与强运动适应性的航天星跟踪场景
事件传感器（EBS）因其低延迟和低功耗，在星体追踪中展现出巨大潜力，但以往研究仅在简化信号模型的仿真中评估。本文提出一种新型事件传感器星体追踪算法，基于对EBS电路的分析与扩展卡尔曼滤波（EKF）。我们使用真实夜空数据定量评估该方法，并与空间级主动像素传感器（APS）星跟踪器结果对比。实验表明，由于更优的信号建模与状态估计，本方法精度较现有方法提升一个数量级，同时提供更高频率更新和更强运动容忍能力。我们公开了全部代码及首个与APS解同步的事件数据集。
原文摘要 · Abstract (English)
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.
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