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激增，使得碰撞风险与空间碎片累积问题已迫在眉睫。应对这场危机，特别是处理非合作型及未识别空间碎片，具有极其重要的战略意义。本文致力于实现自主化小型追踪卫星集群对目标几何构型的确定，并为近地轨道交会操作的安全飞行轨迹规划提供支持。研究聚焦于您只需看一次v5（YOLOv5）目标检测模型在轨应用的可能性——该模型经过专门训练用于卫星组件识别。尽管该模型展现出应用潜力，但其固有的不可解释性制约了人工理解能力，而这正是验证安全关键任务算法的核心要素。为解析其决策过程，我们提出基于熵值知识提取的概率解释（PEEK）方法，该方法运用信息论原理对模型隐藏层中的潜在表征进行深度分析。通过软硬件协同仿真实验，PEEK成功揭示了模型的决策机制，帮助识别其优势、局限性及潜在偏差。