A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry

Operational anomaly detection in spacecraft telemetry typically requires labeled historical anomalies or extended warm-up periods. These requirements are rarely met in practice. We propose an unsupervised, deployment-ready framework that produces predictions from the second month of operation without any labels, prior fault knowledge, or mission-specific tuning. The approach combines incremental monthly retraining, statistical model selection, and adaptive Extreme Value Theory (EVT) thresholding for false alarm control. On the ESA Anomalies Dataset (ESA-AD), it achieves $F_{0.5}=0.700$ on Mission1 and $F_{0.5}=0.698$ on Mission2 under strict chronological evaluation.

Many recent multivariate time series anomaly detection (MTSAD) models incorporate cross-channel modeling, under the implicit assumption that the structure of anomalies may be spread across multiple channels. We evaluate this assumption on eight widely used public benchmarks by introducing a per-segment diagnostic framework that flags, for each labeled anomaly, whether at least one channel deviates individually from its normal history, whether the cross-channel correlation structure changes, or both. The framework shows that no cross-channel rupture occurs without an accompanying univariate deviation across a range of reasonable thresholds. A complementary metric also reveals that on six of the eight benchmarks, at least half of the labeled anomaly segments deviate univariately on 89% to 100% of their timesteps, reaching 100% on three of these datasets. To verify that our framework captures cross-channel structure when present, we construct synthetic data of phase-shifted sinusoidal channels with shared noise. Each anomalous segment is altered through one of two channel-wise corruptions that preserve the per-channel marginal distribution while breaking cross-channel structure, and our framework correctly characterizes these segments as cross-channel-only. On these data, channel-dependent (CD) models successfully exploit the cross-channel signal whereas channel-independent (CI) ones fail. The CI/CD comparison of a recent SOTA detector on real benchmarks further confirms that CD modeling brings no measurable gain. We conclude that current MTSAD benchmarks are unsuitable for validating cross-channel modeling capabilities, and we call for the development of more structurally diverse evaluation sets. The code for this study is publicly available.

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_v1), an Interacting Multiple Model Unscented Kalman Filter (IMM-UKF) bank, and a supplemental-element calibration step (supGP), to produce labels at a scale unavailable from any single source. Applied to 232M Two-Line Element (TLE) records spanning 60 years, the cascade yields 8.6M labeled sequences of length 50 (430M timesteps) over 11 features that include explicit time encoding and full mean-element state. On overlapping satellites, IMM-UKF surfaces 42.6x more anomalies than rule_v1 alone. We train a 6.5M-parameter Transformer in two stages, achieving a maneuver recall of 55.4% and decay recall of 62.8% on a held-out test set. An ablation on the time-delta feature alone yields a 107% relative improvement in decay recall. We frame the resulting model as a high-recall triage classifier whose role is to surface candidate events for downstream filtering, not to issue final attributions, and discuss the path toward a Neural-ODE-based orbital world model.