GNSS Spoofing Detection Based on Adaptive Kinematic Model for IMU-Equipped Vehicles

Yimin Ma, Zhenyang Wu, Yimin Deng, Mingquan Lu, Hong Li

Abstract:	Global Navigation Satellite System (GNSS) is indispensable for providing accurate Position, Velocity, and Timing (PVT) information in modern vehicular applications, while it is susceptible to spoofing attacks which can lead to incorrect PVT results and potentially severe consequences. Considering that Inertial Measurement Unit (IMU) is widely integrated with GNSS for vehicular navigation systems, IMU-aided GNSS spoofing detection methods have attracted more attention. However, as an efficient and practical way to detect spoofing, existing Independent Consistency Monitoring (ICM) methods often face challenges due to the noise in low-cost vehicle-mounted IMUs and the concealment of spoofing attacks. To address these limitations, this paper proposes an Adaptive Kinematic Model-based (AKM-based) spoofing detection method for IMU-equipped vehicles. Kinematic models, which reveal the maneuver information of vehicles, can provide physically interpretable constraints for spoofing deviations and contribute to improving detection performance. Given possible under-fitting issues caused by inappropriate model selection, the paper utilizes adjusted determination coefficient, i.e. adjusted R2 , to adaptively determine the order of kinematic models across diverse driving scenarios. Vehicular experiments demonstrate that the AKM-based method significantly outperforms conventional approaches, and it can improve detection performance by over 30%.
Published in:	Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025) September 8 - 12, 2025 Hilton Baltimore Inner Harbor Baltimore, Maryland
Pages:	603 - 611
Cite this article:	Ma, Yimin, Wu, Zhenyang, Deng, Yimin, Lu, Mingquan, Li, Hong, "GNSS Spoofing Detection Based on Adaptive Kinematic Model for IMU-Equipped Vehicles," Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025), Baltimore, Maryland, September 2025, pp. 603-611. https://doi.org/10.33012/2025.20400
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