Spoofing Detection by Distortion of the Correlation Function

Michael Turner, Stephen Wimbush, Christoph Enneking, Andriy Konovaltsev

Peer Reviewed

Abstract: Open service GNSS signals are vulnerable to a spoofing attack where counterfeit signals are transmitted to deliberately mislead a user receiver. When the spoofer tries to capture the receiver’s tracking loops or pull them away from the genuine signals the correlation function becomes distorted or multiple correlation peaks are present. This effect is utilised by a special class of GNSS receiver which uses block processing with FFT correlators on each satellite tracking channel. The Airbus Multiple Tracking Locked Loop (MTLL) receiver is such a receiver and has the full 2D delay-Doppler correlation function available. To detect a spoofing attack, this paper proposes a method of decomposing the correlation function to estimate the parameters of the radio propagation channel experienced by a GNSS satellite signal. Once the channel parameters have been established, a maximum likelihood ratio test is performed to identify anomalous channel conditions and raise a spoofing alarm. The method has been tested against the simulations of representative scenarios. In addition the University of Texas Spoofing Battery data sets and data from a live signal testing have been included. From the test results, an evaluation is made of the false alarm and missed detection probabilities along with the time to alert. This allows conclusions to be drawn about the potential of the proposed method as an anti-spoofing countermeasure in safety-critical GNSS applications.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 566 - 574
Cite this article: Turner, Michael, Wimbush, Stephen, Enneking, Christoph, Konovaltsev, Andriy, "Spoofing Detection by Distortion of the Correlation Function," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 566-574. https://doi.org/10.1109/PLANS46316.2020.9110173
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