GMM-based GNSS spoofing detector using double differential phase measurement

Abstract

GNSS spoofing is an intentional interference wherein a GNSS receiver tracks counterfeit signals, resulting in incorrect outcome positions. This is the most dangerous type of intervention in the GNSS technology. In this study, we introduce a novel method for detecting spoofing signals using a Gaussian mixture model (GMM) on the double-carrier phase difference (DD) created by two independent receivers. The DD can completely eliminate errors caused by the satellite clock, receiver clock, and atmospheric layers; hence, the signal angle of arrival (AoA) is clearly expressed in the DD measurement. We utilized the GMM to model the probability density function of the DD measurement computed from the phase measurement of the receivers. Theoretically, AoA values of an authentic signal change over time owing to the nature of the signal broadcast from an orbiting satellite. However, fake signals are often transmitted from the generator, resulting in a central distribution of the corresponding AoA values. Existing studies deal with the spoofing detection problem using the above theoretical assumptions. However, it is practical to broadcast spoofing signals from several sources, and random noise can be mixed into the generated phase to render the DD measurement noisy. In such complicated scenarios, existing approaches are not sufficiently robust to detect non-authentic signals. Another observation is that because real satellites are moving in fixed orbits, there should be a correlation among the AoA values of the signals coming from these satellites. In contrast, counterfeit signals (with the main purpose of causing the wrong position or noisy phase) do not follow the pattern of the real signals. Therefore, instead of using the above hard assumption about the signal AoA, we propose a statistical model GMM to learn the hidden relationship of the AoA values among real signals from different real satellites, which are then used to detect spoofing signals.