[2603.29375] Deep Learning-Based Anomaly Detection in Spacecraft Telemetry on Edge Devices

Computer Science > Machine Learning arXiv:2603.29375 (cs) [Submitted on 31 Mar 2026] Title:Deep Learning-Based Anomaly Detection in Spacecraft Telemetry on Edge Devices Authors:Christopher Goetze, Tim Schlippe, Daniel Lakey View a PDF of the paper titled Deep Learning-Based Anomaly Detection in Spacecraft Telemetry on Edge Devices, by Christopher Goetze and 2 other authors View PDF HTML (experimental) Abstract:Spacecraft anomaly detection is critical for mission safety, yet deploying sophisticated models on-board presents significant challenges due to hardware constraints. This paper investigates three approaches for spacecraft telemetry anomaly detection -- forecasting & threshold, direct classification, and image classification -- and optimizes them for edge deployment using multi-objective neural architecture optimization on the European Space Agency Anomaly Dataset. Our baseline experiments demonstrate that forecasting & threshold achieves superior detection performance (92.7% Corrected Event-wise F0.5-score (CEF0.5)) [1] compared to alternatives. Through Pareto-optimal architecture optimization, we dramatically reduced computational requirements while maintaining capabilities -- the optimized forecasting & threshold model preserved 88.8% CEF0.5 while reducing RAM usage by 97.1% to just 59 KB and operations by 99.4%. Analysis of deployment viability shows our optimized models require just 0.36-6.25% of CubeSat RAM, making on-board anomaly detection practical even on hi...