JaGuard recasts GNSS jamming mitigation as dynamic graph regression and uses a Heterogeneous Graph ConvLSTM to estimate 2D positional deviation, achieving MAEs of 2.26-2.61 cm on mixed-power real datasets and remaining stable under severe jamming and data starvation.
Chen, et al., An improved vmd-lstm model for time-varying gnss time series prediction with temporally correlated noise [j]
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JaGuard: Position Error Correction of GNSS Jamming with Deep Temporal Graphs
JaGuard recasts GNSS jamming mitigation as dynamic graph regression and uses a Heterogeneous Graph ConvLSTM to estimate 2D positional deviation, achieving MAEs of 2.26-2.61 cm on mixed-power real datasets and remaining stable under severe jamming and data starvation.