Starlink Orbit Anomaly Detection with Wavelet-Kalman Filtering and Compensated Propagation

The rapid deployment of low-Earth-orbit mega-constellations has increased the demand for reliable and scalable orbit-anomaly monitoring. Existing methods are vulnerable to heavy-tailed measurement errors, maneuver-induced propagation drift, and the anisotropic uncertainty of short observation arcs. This study proposes an uncertainty-aware Starlink monitoring framework that combines residual-domain wavelet shrinkage with a Huber-weighted adaptive two-body extended Kalman filter to suppress non-Gaussian contamination without obscuring abrupt state changes. A linear altitude correction and a quadratic phase-time correction are introduced into Simplified General Perturbations-4 propagation to compensate for maneuver-related forecast drift. A cross-time covariance model and a joint normalized innovation squared test are further constructed for uncertainty-aware short-arc maneuver sensing, while hierarchical evidence fusion supports anomaly detection and event interpretation. Across paired experiments, the filtering chain reduces position root-mean-square error from 752.4 ± 77.7 m to 175.9 ± 9.9 m, and compensated propagation reduces the 72 h prediction error from 128.7 km to 19.6 km. The detector achieves 93.4% accuracy with a 2.7% false-alarm rate and maintains empirical short-arc false-alarm probabilities near the nominal one percent level. These results demonstrate a consistent engineering link between catalog-scale screening and uncertainty-aware short-arc maneuver sensing.