Abstract
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.
Highlights
With the continuous development of Artificial Intelligence (AI), Internet of ings (IoT), and high-performance computing devices in intelligent transportation systems [1], autonomous vehicles (AVs) have become one of the focus research topics in the last decade
For a small Unmanned Aerial Vehicle (UAV), a 3D local pose estimation system has been presented in [4] where the system is realized by fusing 3D position estimations using a loosely coupled extended Kalman filter (EKF) architecture. e data come from an ultra-wideband transceiver network, an inertial measurement unit sensor, and a barometric pressure sensor
By solving several matrix difference equations, the upper bound of estimation error covariance is guaranteed and correctly updated during the recursive process. e contributions are summarized as follows: (1) e modeling of the system takes the occurrence of random deception attacks into account, such that the secure dynamic pose estimation problem has been formulated for autonomous vehicles
Summary
With the continuous development of Artificial Intelligence (AI), Internet of ings (IoT), and high-performance computing devices in intelligent transportation systems [1], autonomous vehicles (AVs) have become one of the focus research topics in the last decade. Security and Communication Networks estimation problem under cyberattacks to deal with the possible sensor attacks, and an EKF reconfiguration scheme has been designed to mitigate the influence of sensor attacks. (1) e modeling of the system takes the occurrence of random deception attacks into account, such that the secure dynamic pose estimation problem has been formulated for autonomous vehicles (2) e proposed unscented Kalman-type secure recursive estimator provides a theoretically proved upper bound for error covariance matrices with stable and efficient state estimation (3) e feasibility and effectiveness of the proposed approach are verified in both a simulated model and the practical AV system, where single and multiple attacks have been considered in experimental design.
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