We consider the problem of detecting spoofing attacks for a GNSS receiver in space, orbiting around the Earth. Since a receiver in space cannot leverage the presence of so called signals of opportunity, it must rely on detecting anomalies in the signal itself and checking the consistency of its measurements with the computed orbital position. We consider three different consistency checks: on the overall received GNSS signal power at the front-end; on the estimated carrier-to-noise ratio (C/N-{o}) for the signal coming from each satellite in view; on the final computed position at the receiver output. Moreover, we devise a fusion method that combines soft outputs from the three checks to provide a more reliable and robust detection. The proposed techniques are tested in a realistic simulation environment showing that, although the position consistency check is by far the most reliable, the proper fusion of the soft information from all three allow to further improve the detection rates in different conditions significantly.
Detection of GNSS Spoofing by a Receiver in Space via Fusion of Consistency Metrics
Chiarello L.;Guglielmi A. V.;Laurenti N.;
2020
Abstract
We consider the problem of detecting spoofing attacks for a GNSS receiver in space, orbiting around the Earth. Since a receiver in space cannot leverage the presence of so called signals of opportunity, it must rely on detecting anomalies in the signal itself and checking the consistency of its measurements with the computed orbital position. We consider three different consistency checks: on the overall received GNSS signal power at the front-end; on the estimated carrier-to-noise ratio (C/N-{o}) for the signal coming from each satellite in view; on the final computed position at the receiver output. Moreover, we devise a fusion method that combines soft outputs from the three checks to provide a more reliable and robust detection. The proposed techniques are tested in a realistic simulation environment showing that, although the position consistency check is by far the most reliable, the proper fusion of the soft information from all three allow to further improve the detection rates in different conditions significantly.File | Dimensione | Formato | |
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