Analytic Models of a Loosely Coupled GNSS/INS/LiDAR Kalman Filter Considering Update Frequency Under a Spoofing Attack

Hostile spoofing attacks on the global navigation satellite system (GNSS) receiver increase the risk of catastrophic consequences to autonomous driving systems. This article addresses the problem of the vulnerability of the Kalman filter (KF) under spoofing attack. A state-of-the-art spoofing attack method based on maximizing the lateral deviation is utilized for verification and analysis. To analyze the vulnerability in actual road scenarios better, an analytic error model of the mechanism of GNSS spoofing is derived. Except for the uncertainty of the initial MSF state, the uncertainty of light detection and ranging (LiDAR), and the uncertainty of GNSS, a new factor in spoofing attacks, the update frequency of different sensors, is investigated in this article, which, in fact, is a key factor to increase the immunity multisensors' fusion (MSF) systems. Experiments were performed in a typical urban scenario of the KAIST dataset. When the update frequency ratios between GNSS and LiDAR are 1, 2, 5, and 10, successful spoof attacks can be performed if the standard deviation (STD) of GNSS is smaller than 4, 2.7, 1.1, and 0.7 m, respectively. Therefore, experiments confirm that the uncertainty of GNSS and the update frequency ratio between LiDAR and GNSS are critical for spoofing attacks, which provides an indication for designing a defense strategy in the future.