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.

翻译：检测低地球轨道（LEO）卫星群体快速扩张中的轨道异常（如机动、大气衰减和姿态突变），是规避碰撞、预测衰减和交汇筛查的前提。瓶颈并非建模能力而是标签：缺乏公开的轨道异常真实语料库，人工审核无法扩展至约10^4颗活跃卫星，而纯规则检测器以牺牲召回率为代价过度追求精度，导致其对大多数行为异常视而不见。我们提出一种多层标签级联方法，整合三种保真度递增的弱监督源：快速物理规则集（rule_v1）、交互多模型无迹卡尔曼滤波（IMM-UKF）组和补充元素校准步骤（supGP），从而生成单源无法企及的大规模标签。将该方法应用于覆盖60年的2.32亿条双行元素（TLE）记录后，级联过程产出860万条长度为50的标注序列（4.3亿时间步），包含11个特征（含显式时间编码和完整平均元素状态）。在重叠卫星中，IMM-UKF发现的异常数量是rule_v1的42.6倍。我们采用两阶段训练6.5M参数的Transformer模型，在保留测试集上实现机动召回率55.4%、衰减召回率62.8%。仅对时间差特征进行消融实验后，衰减召回率相对提升107%。我们将所得模型定位为高召回率分诊分类器，其职责是发现待下游过滤的候选事件而非输出最终归因，并探讨了迈向基于神经ODE的轨道世界模型的路径。