UKF-Based Vehicle Pose Estimation under Randomly Occurring Deception Attacks

Considering various cyberattacks aiming at the Internet of Vehicles (IoV), secure pose estimation has become an essential problem for ground vehicles. This paper proposes a pose estimation approach for ground vehicles under randomly occurring deception attacks. By modeling attacks as signals added to measurements with a certain probability, the attack model has been presented and incorporated into the existing process and measurement equations of ground vehicle pose estimation based on multisensor fusion. An unscented Kalman filter-based secure pose estimator is then proposed to generate a stable estimate of the vehicle pose states; i.e., an upper bound for the estimation error covariance is guaranteed. Finally, the simulation and experiments are conducted on a simple but effective single-input-single-output dynamic system and the ground vehicle model to show the effectiveness of UKF-based secure pose estimation. Particularly, the proposed scheme outperforms the conventional Kalman filter, not only by resulting in more accurate estimation but also by providing a theoretically proved upper bound of error covariance matrices that could be used as an indication of the estimator's status.

Automated summary

This paper proposes a pose estimation approach for ground vehicles under randomly occurring deception attacks, modeling attacks as signals added to measurements and utilizing an unscented Kalman filter to generate stable pose state estimates. Experimental results demonstrate that this method outperforms the traditional Kalman filter by providing more accurate estimates and guaranteeing an upper bound on error covariance.