On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities

Lorenzo Capelli; Leandro de Souza Rosa; Maurizio De Tommasi; Livia Manovi; Andriy Enttsel; Mauro Mangia; Riccardo Rovatti; Ilaria Pinci; Carlo Ciancarelli; Eleonora Mariotti; Gianluca Furano

arXiv · 2604.08424

On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities

Lorenzo Capelli·Leandro de Souza Rosa·Maurizio De Tommasi·Livia Manovi·Andriy Enttsel·Mauro Mangia·Riccardo Rovatti·Ilaria Pinci·Carlo Ciancarelli·Eleonora Mariotti·Gianluca Furano

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Abstract

The increasing autonomy of spacecraft demands fault-detection systems that are both reliable and explainable. This work addresses eXplainable Artificial Intelligence for onboard Fault Detection, Isolation and Recovery within the Attitude and Orbit Control Subsystem by introducing a framework that enhances interpretability in neural anomaly detectors. We propose a method to derive low-dimensional, semantically annotated encodings from intermediate neural activations, called peepholes. Applied to a convolutional autoencoder, the framework produces interpretable indicators that enable the identification and localization of anomalies in reaction-wheel telemetry. Peepholes analysis further reveals bias detection and supports fault localization. The proposed framework enables the semantic characterization of detected anomalies while requiring only a marginal increase in computational resources, thus supporting its feasibility for on-board deployment.

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Lorenzo Capelli, Leandro de Souza Rosa, Maurizio De Tommasi, Livia Manovi, Andriy Enttsel, Mauro Mangia, Riccardo Rovatti, Ilaria Pinci, Carlo Ciancarelli, Eleonora Mariotti, Gianluca Furano. 2026-04-09. On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities. https://arxiv.org/abs/2604.08424

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