Global Navigation Satellite Systems (GNSS) face growing disruption from intentional jamming, undermining critical infrastructure where precise positioning and timing are essential. Current position error correction (PEC) methods mainly focus on multi-path propagation errors and fail to exploit the spatio-temporal coherence of satellite constellations. We recast jamming mitigation as a dynamic graph regression problem. We propose Jamming Guardian (JaGuard), a receiver-centric deep temporal graph network that estimates and corrects jamming-induced positional drift at fixed locations like roadside units. Modeling the satellite-receiver scene as a heterogeneous star graph at each 1 Hz epoch, our Heterogeneous Graph ConvLSTM fuses spatial context (SNR, azimuth, elevation) with short-term temporal dynamics to predict 2D positional deviation. Evaluated on a real-world dataset from two commercial receivers under synthesized RF interference (three jammer types, -45 to -70 dBm), JaGuard consistently yields the lowest Mean Absolute Error (MAE) compared to advanced baselines. Under severe jamming (-45 dBm), it maintains an MAE of 2.85-5.92 cm, improving to sub-2 cm at lower interference. On mixed-power datasets, JaGuard surpasses all baselines with MAEs of 2.26 cm (GP01) and 2.61 cm (U-blox 10). Even under extreme data starvation (10% training data), JaGuard remains stable, bounding error at 15-20 cm and preventing the massive variance increase seen in baselines. This confirms that dynamically modeling the physical deterioration of the constellation graph is strictly necessary for resilient interference correction.

翻译：全球导航卫星系统面临日益严重的有意干扰威胁，这种干扰会破坏依赖精确定位与授时的关键基础设施。现有位置误差校正方法主要关注多径传播误差，未能充分利用卫星星座的时空相干性。本文将干扰抑制重新定义为动态图回归问题，提出以接收机为中心的深度时序图网络——干扰卫士（JaGuard），用于估计并校正路边单元等固定位置的干扰诱导定位漂移。通过将卫星-接收机场景建模为每秒历元下的异构星型图，我们的异构图卷积长短期记忆网络融合空间上下文（信噪比、方位角、仰角）与短期时序动态特征，预测二维位置偏差。基于两台商用接收机在合成射频干扰（三种干扰类型，-45至-70 dBm）下的真实数据集评估，JaGuard相比先进基线方法始终获得最低平均绝对误差（MAE）。在强干扰（-45 dBm）条件下，其MAE维持2.85-5.92厘米，低频干扰下可提升至亚厘米级。在混合功率数据集上，JaGuard以MAE=2.26厘米（GP01）和2.61厘米（U-blox 10）超越所有基线方法。即使在极端数据匮乏（仅10%训练数据）状态下，JaGuard仍保持稳定，将误差控制在15-20厘米范围内，并有效抑制基线方法中出现的大幅方差增长。这证实了动态建模星座图物理退化对于实现鲁棒性干扰校正具有必要性。