面向无人智能小车的双验证安全定位方法OA北大核心CSTPCD

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 de-livery 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 moni-toring points for verification.Once an abnormal position was detected,an alert would be issued,ensuring the normal op-eration 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 bet-ter than existing benchmark algorithms.