Defending Against GNSS Jamming and Spoofing by Multi-Sensor Integration 

Project Description: While GNSS is the primary means to provide absolute position information in transportation systems, radio frequency methods to detect anomalous GNSS signals may not enable their exclusion in all events. The multiple sensors incorporated into advanced vehicles and transportation systems offer unique opportunities to combat nefarious activities such as GNSS jamming and spoofing to maintain PNT accuracy and integrity. This project will involve three research directions related to GNSS multi-sensor augmentation.  

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1. INS Augmentation. Spoofing relies on accurate prediction of a victim GNSS antenna’s future trajectory to compute and broadcast RF signals to fool the receiver tracking loops on the target vehicle. The vehicle sprung mass, lane curvature, and human driving all add uncertainty around the predicted trajectory, making it difficult to predict GNSS antenna motion. Therefore, we question the ability of a spoofer to predict a target vehicle trajectory with sufficient accuracy to avoid detection. We will investigate whether an integrated INS/GNSS with a position-domain innovation sequence detector is sensitive enough to detect the onset of spoofing by monitoring the accumulated time history of normalized KF innovations. 

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2. Virtual Augmentation for Ground Vehicles. Unlike aircraft, ground vehicles are subject to kinematic constraints. For example, their lateral (“cross-track”) motion is subject to nonholonomic constraints (i.e., under no-slip conditions, the rear wheels can only move longitudinally, not laterally). Encoders on the four wheels provide information about wheel velocity and slip. Both the wheel speed information and the kinematic constraints can be incorporated into PNT algorithms. We will investigate the utility of such methods for detection of anomalous PNT information (e.g., jamming and spoofing). 

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3. Multi-sensor Augmentation. Jamming and spoofing only affect the GNSS receiver; therefore, information extracted from additional on-board sensors (e.g., cameras, lidar, radar, ultrasound, IMU, wheel encoders) offer unique opportunities for enhancing PNT resilience. Research will focus on PNT solutions incorporating data from the diversity of sensors to improve both PNT accuracy and detection of anomalous PNT information from all sensors.  

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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 how multi-sensor integration can reduce the risk from spoofing to enhance the resilience of connected and automated vehicles. 

Outputs:  In this project, we will: 

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• Investigate and develop new methods by which a vehicle equipped with an integrated INS/GNSS can detect and accommodate spoofing and other anomalous behaviors. 

• Investigate and develop new methods incorporating kinematic motion constraints with related sensor information to detect and accommodate spoofing and other anomalous behaviors.  

• Investigate and develop new methods incorporating the suite of sensors on advanced vehicles to detect and accommodate spoofing and other anomalous behaviors.  

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We expect interest in this research from Connected and Autonomous Vehicle (CAV) navigation system designers, CAV application designers, and CAV manufacturers. We will actively encourage those on CARNATIONS External Advisory Board to contribute feedback and collaborate throughout the effort.  

Outcomes/Impacts: CAV’s offer a unique potential to address negative aspects of transportation systems (e.g., congestion, emissions) as well as enhancing safety, if the navigations systems can continuously and reliably provide accurate vehicle state information at the bandwidth and sample rates required for vehicle control. The aim of this project is to rigorously analyze and demonstrate the extent to which the multiple sensors onboard CAVs can meet the application requirements.  

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