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.

通过利用信息论分析模型中隐藏层中的潜在表达来解释决策过程，研究论文使用了 PEEK 方法，使得我们能够了解 You Only Look Once v5（YOLOv5）目标检测模型在解决低地球轨道（LEO）中的非合作和未鉴别空间碎片的自主小型追逐卫星的几何确定和安全飞行轨迹规划方面的优势、局限性和偏差。