用事件相机+堆叠CNN实时检测太空碎片，提升避障能力
An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras
- 结合事件相机与堆叠CNN算法，实时分析运动微弱目标
- 在地面数据上提升信噪比，有效识别微弱移动物体
- 适用于航天态势感知，适合卫星避障系统部署
空间碎片构成重大威胁，推动主动与被动缓解策略的研究。本文提出一种创新的碰撞规避系统，利用事件相机——一种适用于空间态势感知（SSA）与空间交通管理（STM）的新成像技术——实时分析事件数据，检测微弱运动目标。该系统采用先前用于流星探测的堆叠CNN算法，在地面数据测试中展现出显著提升信噪比的能力，为星载成像提供可行方案，有望改善STM/SSA运行效率。
原文摘要 · Abstract (English)
Space debris poses a significant threat, driving research into active and passive mitigation strategies. This work presents an innovative collision avoidance system utilizing event-based cameras - a novel imaging technology well-suited for Space Situational Awareness (SSA) and Space Traffic Management (STM). The system, employing a Stack-CNN algorithm (previously used for meteor detection), analyzes real-time event-based camera data to detect faint moving objects. Testing on terrestrial data demonstrates the algorithm's ability to enhance signal-to-noise ratio, offering a promising approach for on-board space imaging and improving STM/SSA operations.
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