|Abstract:||GNSS receivers are highly vulnerable to structural interference signals such as spoofing and meaconing. A spoofing attack based on a set of synthesized GNSS signals, is an effective means of providing bogus position estimates to a victim receiver. Several spoofing countermeasure techniques to address different attack types using single or multiple antenna have been proposed. Both single and multiple antenna techniques focus on specific features of spoofing signals that can separate them from the authentic ones. Multiple antenna techniques can observe spoofing signals in ways that a single antenna cannot, and as a result they strengthen detection capabilities. Single antenna based spoofing detection metrics are implemented in the pre-despreading or post-despreading layers of a GNSS receiver and are most effective when both spoofing and authentic signals are present. Pre-despreading and intermediate frequency signal monitoring metrics have been used to detect the presence of excessive amount of power in GNSS bands. These metrics rely on the assumption that spoofing signals are more powerful than the authentic ones and that a successful spoofing attack transmits several GNSS-like signals. Post-despreading methods are used to detect an abnormal behavior in acquisition and tracking levels which is caused by the presence of both spoofing and authentic signals. In many practical scenarios, a spoofer generates multiple GNSS signals and transmits them using a single antenna. As such, spoofed PRNs are spatially correlated since they all experience the same propagation channel. This feature can be used to discriminate them from the spatially distributed authentic signals. More specifically, the counterfeit signals sourced from a single transmit antenna have the same spatial signature, which means that all the signals experience the same channel variation in the spatial domain. This can be used as a metric to detect a spoofing attack. A key advantage of multiple antenna over a single antenna spoofing detection is that it can detect a spoofing attack in the absence of authentic signals (e.g. covered antenna case). Additionally, when both authentic and spoofed signals are present, a multiple antenna based detection method can identify which particular PRNs are spoofed. This paper demonstrates a spoofing detection module implemented on a dual-antenna variant of NovAtel’s OEM7 generation of GNSS receivers. The proposed architecture enables the receiver to detect a spoofing attack, as well as discriminate and classify spoofed PRNs from the authentic ones. The detector utilizes pre-despreading and post-despreading methods using a multi-layer detection strategy. The spatial processing detection metric uses two spatially separated antenna and is based on single and double-difference carrier phase observations. The operation of the proposed receiver structure will be tested in a real-world spoofing scenario. A hardware simulator is used as a spoofing generator, combined with the authentic signals collected from two spatially separated outdoor antennas. The test results show that the proposed technique can successfully detect and classify the spoofing and authentic PRNs. The advantage of a dual-antenna spoofing detection method will be compared to that of a single antenna in various spoofing attack scenarios. The detection performance of the dual-antenna spoofing detection methodology as a function of the antenna spacing will be characterized. The mean time to detect and probability of false detection in absence of spoofing attack will be evaluated in various GNSS operation environments.|
Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020)
September 21 - 25, 2020
|Pages:||1515 - 1532|
|Cite this article:||
Broumandan, Ali, Taylor, Thomas, Anklovitch, Darrell, Kennedy, Sandy, "Robust Dual -Antenna Receiver: Jamming/Spoofing Detection and Mitigation," Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020), , September 2020, pp. 1515-1532.
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