Tongxin xuebao (Jun 2024)
Dual-verified secure localization method for unmanned intelligent vehicles
Abstract
Unmanned intelligent vehicles are exposed to high risks of network attack, hardware attack, operating system attack and software attack. They are susceptible to physical or remote security attacks, causing it to deviate from the delivery trajectory and fail the delivery task, or even be manipulated to disrupt normal operation of the factory. To address this problem, a dual-verified secure localization method for unmanned intelligent vehicles was proposed. The existing Wi-Fi network infrastructure was utilized by the vehicles for fingerprinting localization and a feature fusion strategy was designed to realize the dynamic fusion of Wi-Fi and magnetic field fingerprints. Multiple environmental monitoring points were deployed to collect the sound signals made by vehicles to calculate the position based on time difference of arrival and spatial segmentation method. Then the location reported by the vehicle was compared with the result of monitoring points for verification. Once an abnormal position was detected, an alert would be issued, ensuring the normal operation of the unmanned intelligent vehicles. The experimental results in the real indoor scenarios show that the proposed method can effectively track the positions of the target unmanned intelligent vehicle, and the positioning accuracy is better than existing benchmark algorithms.
