Using a Sensor-Health-Aware Resilient Fusion for Localization in the Presence of GPS Spoofing Attacks

The integration of unmanned air systems and autonomous vehicles in various industries has led to a heightened focus on cybersecurity, particularly regarding Global Positioning System (GPS) spoofing attacks. This research proposes an algorithm to detect and deemphasize GPS when spoofed and then perform graceful recovery when GPS recovers. By integrating data from GPS, Inertial Measurement Units (IMUs), and low-resolution onboard sensors, the research applies the recently developed Sensor-Health Aware Resilient Fusion (SHARF) algorithm that maintains positional accuracy despite compromised GPS data. The algorithm's health monitoring component continuously evaluates sensor integrity, applying a convex combination of Kalman Filters and Covariance Intersection methods to ensure unbiased and consistent estimates of the vehicle's state. The impact of the proposed algorithms in enhancing the reliability and security of navigation systems is demonstrated with emulated GPS spoofing attacks on experimental data.