Cooperative Localization of Connected Vehicles With Attack Detection and Secure Fusion

• Published in: IEEE transactions on vehicular technology Vol. 74; no. 4; pp. 5713 - 5723
• Main Authors: Liu, Jiageng, Guo, Ge, Sun, Xiaozheng, Li, Zengbo, Lin, Haodong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; attack detection; Connected vehicles; Detection algorithms; Estimation; Global Positioning System; Kalman filters; Kinematics; Localization; Location awareness; Position measurement; secure fusion; Simulation; Urban environments; Vehicle-to-everything; Vehicles; Wheels
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2024.3521388

The vulnerability from cyberattack undermines the security in cooperative localization of connected vehicles. This paper presents a localization framework for connected vehicles subject to false data injection attacks, featuring an attack detection scheme to get rid of abnormal position information from malicious vehicles. To this end, a refined kinematic model, measurement model, and attack model are introduced. By introducing a local Kalman decomposition procedure to extract the position measurements from the collection of connected vehicles, a fusion algorithm is presented to guarantee secure and accurate vehicle localization. The simulation and experimental results show that the method outperforms the state-of-the-art vehicle localization methods in urban environments.