Deep Temporal Graph Networks for Real-Time Correction of GNSS Jamming-Induced Deviations

• URL: http://arxiv.org/abs/2509.14000v1
• Date: Wed, 17 Sep 2025 14:12:36 GMT
• Title: Deep Temporal Graph Networks for Real-Time Correction of GNSS Jamming-Induced Deviations
• Abstract summary: jamming is disrupting Global Navigation Satellite Systems (GNSS)<n>We introduce a receiver-centric deep temporal graph network that predicts, and thus corrects, the receivers horizontal deviation in real time.<n>A single layer Heterogeneous Graph ConvLSTM (HeteroGTM) aggregates one hop context and temporal dynamics over a short history.
• Score: 4.489167091323233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Global Navigation Satellite Systems (GNSS) are increasingly disrupted by intentional jamming, degrading availability precisely when positioning and timing must remain operational. We address this by reframing jamming mitigation as dynamic graph regression and introducing a receiver-centric deep temporal graph network that predicts, and thus corrects, the receivers horizontal deviation in real time. At each 1 Hz epoch, the satellite receiver environment is represented as a heterogeneous star graph (receiver center, tracked satellites as leaves) with time varying attributes (e.g., SNR, azimuth, elevation, latitude/longitude). A single layer Heterogeneous Graph ConvLSTM (HeteroGCLSTM) aggregates one hop spatial context and temporal dynamics over a short history to output the 2D deviation vector applied for on the fly correction. We evaluate on datasets from two distinct receivers under three jammer profiles, continuous wave (cw), triple tone (cw3), and wideband FM, each exercised at six power levels between -45 and -70 dBm, with 50 repetitions per scenario (prejam/jam/recovery). Against strong multivariate time series baselines (MLP, uniform CNN, and Seq2Point CNN), our model consistently attains the lowest mean absolute error (MAE). At -45 dBm, it achieves 3.64 cm (GP01/cw), 7.74 cm (GP01/cw3), 4.41 cm (ublox/cw), 4.84 cm (ublox/cw3), and 4.82 cm (ublox/FM), improving to 1.65-2.08 cm by -60 to -70 dBm. On mixed mode datasets pooling all powers, MAE is 3.78 cm (GP01) and 4.25 cm (ublox10), outperforming Seq2Point, MLP, and CNN. A split study shows superior data efficiency: with only 10\% training data our approach remains well ahead of baselines (20 cm vs. 36-42 cm).

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