The escalating risk of collisions and the accumulation of space debris in Low Earth Orbit (LEO) has reached critical concern due to the ever increasing number of spacecraft. Addressing this crisis, especially in dealing with non-cooperative and unidentified space debris, is of paramount importance. This paper contributes to efforts in enabling autonomous swarms of small chaser satellites for target geometry determination and safe flight trajectory planning for proximity operations in LEO. Our research explores on-orbit use of the You Only Look Once v5 (YOLOv5) object detection model trained to detect satellite components. While this model has shown promise, its inherent lack of interpretability hinders human understanding, a critical aspect of validating algorithms for use in safety-critical missions. To analyze the decision processes, we introduce Probabilistic Explanations for Entropic Knowledge extraction (PEEK), a method that utilizes information theoretic analysis of the latent representations within the hidden layers of the model. Through both synthetic in hardware-in-the-loop experiments, PEEK illuminates the decision-making processes of the model, helping identify its strengths, limitations and biases.

翻译：低地球轨道（LEO）中航天器数量的持续增长导致碰撞风险与空间碎片积累问题日益严峻，已成为关键性关切。应对这一危机，特别是在处理非合作与未识别空间碎片方面，具有极其重要的意义。本文致力于推动自主小型追踪卫星集群在低地球轨道临近操作中实现目标几何构型确定与安全飞行轨迹规划的相关研究。我们探索了在轨应用You Only Look Once v5（YOLOv5）目标检测模型识别卫星部件的可行性。尽管该模型展现出良好潜力，但其固有的可解释性缺失阻碍了人类对决策过程的理解——这对安全关键任务中算法验证至关重要。为分析决策机制，我们提出基于熵的知识提取概率解释方法（PEEK），该方法通过信息论分析模型隐藏层中的潜在表征。在包含半实物仿真的综合实验中，PEEK揭示了模型的决策过程，有助于识别其优势、局限性与偏差。