Multi-vehicle/Infrastructure Jammer/Spoofer Detection and Localization 

Project Description:  This project will follow three paths in parallel, all focused on developing vehicle strategies that provide improved knowledge of and resilience to positioning uncertainty, in particular, of the potential risk of spoofing. The first path is focused on developing resilient connected and automated vehicle (CAV) applications given uncertain PNT services; the second is developing resilience techniques through a multi-agent community approach; and the third is to conduct research on collaborative radio-frequency interference (RFI) localization. 

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1.  The main focus of the first research path is to develop CAV applications that are “aware” of their positioning uncertainty and the potential risk of spoofing, and adapt to make them more robust in terms of safety, mobility, and environmental factors. This will consist of several tasks, including: 1) searching CAV application literature to identify any applications that are adaptable in terms of positioning and spoofing uncertainty; 2) identifying a variety of CAV fundamental maneuvers that can be targeted for position uncertainty adaptive algorithms; 3) designing these adaptive algorithms for a subset of fundamental maneuvers (e.g. vehicle merging), followed by comprehensive testing both in simulation and in the real world; and 4) developing the means for estimating and communicating position uncertainty and the risk of undetected spoofed PNT services. 

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2.  The main focus of the second research path is to develop resiliency techniques using a multi-agent community approach where a diversity of connected vehicles and infrastructure are operating in close proximity. Within this community, the impacts of jamming could be mitigated by community alerts by directing vehicles to switch to non-GNSS PNT or to avoid a particular area. During spoofing, the spoofed GNSS signals would have had to be generated based on only one vehicle’s predicted trajectory; however, they would be received by all vehicles within a given neighborhood of the broadcaster. All other vehicles within the reception volume would be receiving inconsistent GNSS signals, which would enable community detection of spoofing. This research will quantify the performance of this detection approach. We will analyze a number of scenarios and the impact of transportation threats. 

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3. In the third research path, we will demonstrate the ability of multiple connected vehicle receivers to detect and localize a common RFI source. We will determine the circumstances under which such a collaborative RFI detection and localization scheme are possible. For example, if two receivers are within reach of an RFI source, using time-differenced measurements over larger than 100-meter separation distances can enable time-of-arrival localization. Phase differences can be more challenging to achieve but reduce the baseline requirement to meter level. We will quantify the resulting localization performance in example use-cases.  

US DOT Priorities:  This research project directly targets the US DOT’s research priority area of Reducing Transportation Cybersecurity Risks. Specifically, we will be investigating potential risks of spoofing for connected and automated vehicles. We will also develop new techniques to make CAVs more resilient to spoofing attacks. 

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Outputs:  In this jumpstart project, we will: 

• Quantify the jamming detection performance of multiple vehicle and infrastructure receivers and design multi-agent mitigation strategies in the context of a large-scale transportation system.

• Evaluate the spoofing detection performance of multi-vehicle receivers using trajectory diversity.

• Test the RFI localization performance of a multi-agent system, and refine mitigation strategies. 

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We expect interest in this research from CAV application designers and CAV manufacturers and will actively

encourage those on CARNATIONS External Advisory Board to contribute feedback and collaborate throughout the effort.  

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Outcomes/Impacts:  Current CAV applications are “rigid” and do not adapt well to PNT uncertainty and spoofing. It is critical that these dynamic PNT uncertainties be recognized in real-time and that vehicles adapt, if CAV deployments are going to succeed. This project will pave the way for developing new CAV applications that are robust against PNT uncertainties both in time and space. The results of this project will be shared with the DOT, GNSS researchers, industry, and standardization bodies.  

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Final Research Report:  (Upon completion of the project we will a provide link to the final report.)