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 noncooperative 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 object detection model trained to detect satellite components. Although 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 and hardware-in-the-loop experiments, PEEK illuminates the decision-making processes of the model, helping to identify its strengths, limitations, and biases.


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    Title :

    Probabilistic Explanations for Entropic Knowledge Extraction for Automated Satellite Component Detection



    Published in:

    Publication date :

    2025-04-01




    Type of media :

    Conference paper , Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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