Reliable anomaly detection in satellite telemetry is critical for mission success, yet traditional threshold-based methods struggle with complex and evolving patterns. This work presents machine learning (ML) techniques to analyze high-dimensional telemetry data. Evaluations of real-world satellite telemetry datasets demonstrate the potential of ML to enhance spacecraft health monitoring and reduce manual intervention.

AI-Powered Anomaly Detection for Satellite Telemetry / Buccellato, Federico; Nicolini, Davide; Vacca, Eleonora; De Sio, Corrado; Sterpone, Luca. - ELETTRONICO. - (2025), pp. 222-223. (Intervento presentato al convegno CF '25: 22st ACM International Conference on Computing Frontiers tenutosi a Cagliari (ITA) nel 28-30 May 2025) [10.1145/3719276.3727953].

AI-Powered Anomaly Detection for Satellite Telemetry

Buccellato, Federico;Nicolini, Davide;Vacca, Eleonora;De Sio, Corrado;Sterpone, Luca
2025

Abstract

Reliable anomaly detection in satellite telemetry is critical for mission success, yet traditional threshold-based methods struggle with complex and evolving patterns. This work presents machine learning (ML) techniques to analyze high-dimensional telemetry data. Evaluations of real-world satellite telemetry datasets demonstrate the potential of ML to enhance spacecraft health monitoring and reduce manual intervention.
2025
979-8-4007-1528-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2999715