Please use this identifier to cite or link to this item:
https://doi.org/10.1155/2021/5572186
Title: | UKF-Based Vehicle Pose Estimation under Randomly Occurring Deception Attacks | Authors: | Liu, Xinghua Bai, Dandan Lv, Yunling Jiang, Rui Ge, Shuzhi Sam |
Issue Date: | 23-Sep-2021 | Publisher: | Hindawi Limited | Citation: | Liu, Xinghua, Bai, Dandan, Lv, Yunling, Jiang, Rui, Ge, Shuzhi Sam (2021-09-23). UKF-Based Vehicle Pose Estimation under Randomly Occurring Deception Attacks. Security and Communication Networks 2021 : 5572186. ScholarBank@NUS Repository. https://doi.org/10.1155/2021/5572186 | Rights: | Attribution 4.0 International | 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. © 2021 Xinghua Liu et al. | Source Title: | Security and Communication Networks | URI: | https://scholarbank.nus.edu.sg/handle/10635/233253 | ISSN: | 1939-0114 | DOI: | 10.1155/2021/5572186 | Rights: | Attribution 4.0 International |
Appears in Collections: | Elements Staff Publications |
Show full item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
10_1155_2021_5572186.pdf | 3.45 MB | Adobe PDF | OPEN | None | View/Download |
This item is licensed under a Creative Commons License