Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale
Detecting orbital anomalies, such as maneuvers, atmospheric decay, and attitude upsets, across the rapidly growing population of low-Earth-orbit (LEO) satellites is a prerequisite for collision avoidance, decay forecasting, and conjunction screening. The bottleneck is not modeling capacity but labels: there is no public ground-truth corpus of orbital anomalies, manual review does not scale to approximately 10^4 active satellites, and pure rule-based detectors trade recall for precision so aggressively that they are blind to most behavioral anomalies. We present a multi-tier labeling cascade that composes three weak supervision sources of increasing fidelity: a fast physics rule set (rule_...
| Authors | Yong Fu |
|---|---|
| Primary category | cs.DC |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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