JaGuard: Jamming Correction of GNSS Deviation with Deep Temporal Graphs

Global Navigation Satellite Systems (GNSS) are increasingly exposed to intentional jamming, threatening reliability when accurate positioning and timing are most critical. We address this problem by formulating interference mitigation as a dynamic graph regression task and propose JaGuard, a receiver-centric temporal graph neural network that estimates and corrects latitude and longitude errors. At each 1 Hz epoch, the satellite-receiver scene is represented as a heterogeneous star graph with time-varying satellite attributes such as SNR, azimuth and elevation. A single-layer HeteroGCLSTM fuses one-hop spatial context with short-term temporal dynamics to produce a 2D deviation estimate.