Enhanced GNSS Spoofing Detector via Multiple-Epoch Inertial Navigation Sensor Prediction in a Tightly-Coupled System
Liyuan Zhang, Hongbo Zhao, Chao Sun, Lu Bai, Wenquan Feng
Abstract
Global navigation satellite system (GNSS) signals have open structure and weak power, which is vulnerable to spoofing attacks. The Innovation-based detection technique has been proved effective for spoofing detection in an integrated navigation system. However, it employs spoofed priori estimate of pseudorange to construct innovation, showing shortcomings like limited detection probability for slowly-varying spoofing attacks, long time to alarm (TTA), and high false alarm probability. This work proposes an enhanced innovation-based spoofing detector via multiple-epoch inertial navigation sensor (INS) prediction. An additional INS unit is added, which is corrected by extended Kalman Filter (EKF) every <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> epoch. Its output is used to replace the spoofed priori estimate in the EKF and then construct an improved innovation. Compared with the conventional innovation, the improved one can be immune to spoofing during multiple epochs and accumulate more abnormal energy due to spoofing attacks. In addition, an innovation bias mitigation method is presented, which exploits the mean of innovation of previous epochs to predict the immediate innovation bias and thus reduces the probability of false alarm. The overall spoofing detection performance is evaluated using both simulation data and hardware-based experiment data collected in Beihang university. A driving test was also carried out to verify its performance in complex urban conditions. Results show that the proposed detector significantly improves the detection performance under slowly-varying spoofing attacks and reduces the probability of false alarm compared with the conventional detector.