Detection of Replay Attacks to GNSS based on Partial Correlations and Authentication Data Unpredictability

Intentional interference, and in particular GNSS spoofing, is currently one of the most significant concerns of the Positioning, Navigation and Timing (PNT) community. With the adoption of Open Service Navigation Message Authentication (OSNMA) in Galileo, the E1B signal component will continuously broadcast unpredictable cryptographic data. This allows GNSS receivers not only to ensure the authenticity of data origin but also to detect replay spoofing attacks for receivers already tracking real signals with relatively good visibility conditions. Since the spoofer needs to estimate the unpredictable bits introduced by OSNMA with almost zero delay in order to perform a Security Code Estimation and Replay (SCER) attack, the spoofer unavoidably introduces a slight distortion into the signal, which can be the basis of a spoofing detector. In this work, we propose five detectors based on partial correlations of GNSS signals obtained over predictable and unpredictable parts of the signals. We evaluate them in a wide set of test cases, including different types of receiver and spoofing conditions. The results show that one of the detectors is consistently superior to the others, and it is able to detect SCER attacks with a high probability even in favorable conditions for the spoofer. Finally, we discuss some practical considerations for implementing the proposed detector in receivers, in particular when the Galileo OSNMA message structure is used

Multilag Frequency Estimation for High-Order BOC Signals in the Acquisition Stage

In the context of global navigation satellite systems, this paper addresses the problem of refining the Doppler frequency estimation provided in the acquisition stage for highorder binary offset carrier (BOC) signals in post-correlation. The refinement of Doppler frequency must be done because the estimation obtained from the acquisition stage is not usually accurate enough to track the signal in the tracking stage. In this work, we only use the cross-ambiguity function (CAF) created in the acquisition stage to perform the refinement. A least squares estimator has been already applied to mitigate this problem. We propose a new technique, referred to as multilag least squares estimator, which improves the performance of the least squares estimator by exploiting the autocorrelation shape of high-order BOC signals. Moreover, the Cramer-Rao bound and the expected Cramer-Rao bound are derived as benchmark to compare the performance of the least squares and multilag least squares estimators.acceptedVersionPeer reviewe