# JaGuard: Position Error Correction of GNSS Jamming with Deep Temporal Graphs Source: [JaGuard: Position Error Correction of GNSS Jamming with Deep Temporal Graphs]() Collection index: [llms.txt]() Data updated: 2026-04-30T05:00:04.703Z Authors: Ivana Kesi\\'c, Alja\\v{z} Blatnik, Carolina Fortuna, Bla\\v{z} Bertalani\\v{c} Published: 2025-09-17T14:12:36.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Position Error Correction on GP01 receiver CW jamming](): Position error correction; GP01 receiver CW jamming; MAE: 1.27. - [Position Error Correction on GP01 receiver 3×CW jamming](): Position error correction; GP01 receiver 3×CW jamming; MAE: 1.49. - [Position Error Mitigation on U-blox 10 3×CW Jamming](): Position Error Mitigation; U-blox 10 3×CW Jamming; MAE: 2.39. - [Position Error Mitigation on U-blox 10 FM Jamming](): Position Error Mitigation; U-blox 10 FM Jamming; MAE: 1.51. - [Interference Mitigation on Mixed Jamming Dataset U-blox 10 all jamming types pooled](): Interference Mitigation; Mixed Jamming Dataset U-blox 10 all jamming types pooled; MAE (cm): 2.61. - [Interference Mitigation on Mixed Jamming Dataset GP01 all jamming types pooled](): Interference Mitigation; Mixed Jamming Dataset GP01 all jamming types pooled; MAE (cm): 2.26. - [Position Error Mitigation on U-blox CW Jamming 10](): Position Error Mitigation; U-blox CW Jamming 10; MAE: 1.37. ## Original sources and implementation [Original paper]() [PDF]()