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.
| Comments: | 12 pages, 8 figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2509.14000 [cs.LG] |
| (or arXiv:2509.14000v4 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2509.14000 arXiv-issued DOI via DataCite |
From: Ivana Kesić [view email]
[v1]
Wed, 17 Sep 2025 14:12:36 UTC (2,033 KB)
[v2]
Mon, 8 Dec 2025 13:17:21 UTC (2,851 KB)
[v3]
Tue, 24 Mar 2026 16:56:42 UTC (1,366 KB)
[v4]
Tue, 28 Apr 2026 11:22:58 UTC (1,369 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。