UKF‐Based Vehicle Pose Estimation under Randomly Occurring Deception Attacks
Xinghua Liu, Rui Jiang, Badong Chen, Shuzhi Sam Ge · 2022
Various cyberattacks have been aimed at the Internet of Vehicles (IoV), so secure pose estimation has become an essential problem for ground vehicles. This chapter presents 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 is presented and incorporated into the existing process and measurement equations of ground vehicle pose estimation based on multi-sensor fusion. An unscented Kalman filter (UKF)-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. In particular, the proposed scheme outperforms the conventional KF, not only by resulting in more accurate estimation but also by providing a theoretically proven upper bound of error covariance matrices that could be used as an indication of the estimator's status.