aiqbench analysis
JaGNet represents a significant advancement in robust mobile computing by using deep temporal graph networks to detect, classify, and correct GNSS position errors caused by jamming attacks. By operating solely on standard receiver outputs without requiring raw pseudoranges, this method offers a practical solution for critical infrastructure like railway signaling and fleet tracking where location integrity is paramount. The approach effectively bridges the gap between theoretical defense mechanisms and real-world deployment in noisy environments.
JaGNet: Detection, Identification and Position Error Correction of GNSS Jamming with Deep Temporal Graphs
arXiv:2509.14000v5 Announce Type: replace Abstract: Global Navigation Satellite Systems (GNSS) underpin location-dependent mobile computing, such as railway signaling, public transport and fleet tracking, yet face growing disruption from low-cost jammers. Under moderate jamming, low-cost receivers keep reporting a valid fix, shifted by up to several meters and often without an interference flag. Their built-in defenses run in closed firmware, and existing data-driven position error correction methods are designed for multipath, require raw pseudoranges or operate on single epochs. We propose JaGNet, a single-layer heterogeneous graph convolutional LSTM over star graphs of the receiver and its satellites, which, from the standard receiver output alone, detects the jamming, identifies its type and power, and corrects the reported position only when jamming is detected. We evaluate it on new hardware-in-the-loop recordings of a commercial multi-constellation receiver under three jammer types at six powers, at six static locations and on eleven railway and bus routes. JaGNet detects 99.8 to 99.9% of the jammed 10-s windows with at most 1.0% false positives, against 64.7 to 66.2% for the receiver's own monitor, and identifies the jammer type in 95.2 to 96.6% of them. Trained in the laboratory only, it detects 97.7% of the Jammertest 2024 field windows with a C/N0 drop of 10 dB or more. It reduces the mean absolute position error by 67.5% at held-out locations and by 64.0% on held-out route repetitions, where its error is 20.2 cm below that of the strongest baseline, whereas at the static locations the attention-based baselines are within 0.4 cm of it.
Abstract:Global Navigation Satellite Systems (GNSS) underpin location-dependent mobile computing, such as railway signaling, public transport and fleet tracking, yet face growing disruption from low-cost jammers. Under moderate jamming, low-cost receivers keep reporting a valid fix, shifted by up to several meters and often without an interference flag. Their built-in defenses run in closed firmware, and existing data-driven position error correction methods are designed for multipath, require raw pseudoranges or operate on single epochs. We propose JaGNet, a single-layer heterogeneous graph convolutional LSTM over star graphs of the receiver and its satellites, which, from the standard receiver output alone, detects the jamming, identifies its type and power, and corrects the reported position only when jamming is detected. We evaluate it on new hardware-in-the-loop recordings of a commercial multi-constellation receiver under three jammer types at six powers, at six static locations and on eleven railway and bus routes. JaGNet detects 99.8 to 99.9% of the jammed 10-s windows with at most 1.0% false positives, against 64.7 to 66.2% for the receiver's own monitor, and identifies the jammer type in 95.2 to 96.6% of them. Trained in the laboratory only, it detects 97.7% of the Jammertest 2024 field windows with a C/N0 drop of 10 dB or more. It reduces the mean absolute position error by 67.5% at held-out locations and by 64.0% on held-out route repetitions, where its error is 20.2 cm below that of the strongest baseline, whereas at the static locations the attention-based baselines are within 0.4 cm of it.
Machine Learning (cs.LG)
arXiv:2509.14000 [cs.LG]
(or arXiv:2509.14000v5 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2509.14000 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 342 words
