AINeutralarXiv – CS AI · May 126/10
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Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale
Researchers developed a multi-tier labeling system combining physics-based rules, Kalman filtering, and machine learning to detect orbital anomalies across thousands of LEO satellites. The approach generated 8.6M labeled training sequences from 232M historical records, enabling a Transformer model to achieve 55.4% maneuver recall and 62.8% decay recall—addressing a critical gap in space situational awareness infrastructure.