UKF-Based Vehicle Pose Estimation under Randomly Occurring Deception Attacks

Author:

Liu Xinghua1ORCID,Bai Dandan1,Lv Yunling1,Jiang Rui2ORCID,Ge Shuzhi Sam2ORCID

Affiliation:

1. Department of Power Grid Information and Control Engineering, School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China

2. Department of Electrical and Computer Engineering, National University of Singapore, 117583, Singapore

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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Finite-Horizon URTSS-Based Position Estimation for Urban Vehicle Localization;IEEE Sensors Journal;2023-02-15

2. Robust Kalman Filter for Position Estimation of Automated Guided Vehicles Under Cyberattacks;IEEE Transactions on Instrumentation and Measurement;2023

3. References;Multimodal Perception and Secure State Estimation for Robotic Mobility Platforms;2022-08-26

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