Signal Quality Monitoring (SQM) has been widely used as a simple and effective means to detect spoofing attacks on Global Navigation Satellite Systems (GNSS). However, the inherent disadvantages of SQM techniques such as low detection accuracy and poor robustness necessitate the study of new methods. We propose an innovative enhanced SQM method based on the application of a statistical test, known as the Kolmogorov–Smirnov (KS) test, applied to monitor the correlator output of the GNSS receiver to identify the subtle distortions of the correlation function. The simulation results show that the KS test-based method is suitable for detecting spoofing attacks with different power advantages. Compared with seven existing typical spoofing detection methods in applied cases 2, 3, and 7 from the Texas Spoofing Test Battery datasets, the proposed method achieved a higher than 95% detection rate at a false alarm rate of 10%, which is 27.69%, 10.36%, and 6.43% higher than that of the detection method based on weighted second-order central moments, respectively, and the spoofed alarm time delay and computation time are shortened. The proposed method overcomes the performance loss of existing SQM methods and provides excellent detection accuracy and effectiveness, thus, can be used as a potential reliable application solution against spoofing attacks with differing frequency lock modes and power superiorities.

Research on the key anti-spoofing technologies is of great significance to resist the increasingly complex threat of spoofing attacks on global navigation satellite system (GNSS) users’ application services. The purpose of detection technologies is to detect spoofing attacks and these are currently relatively mature. However, the current mitigation technologies rely on an antenna array or external sensor information, which is costly and poor in practicability. Therefore, we propose a new detection–estimation–correction anti-spoofing technique based on the vector tracking loop structure. This technique effectively utilizes the advantages of the vector tracking loop structure to construct equivalent noise bandwidth detection statistics to detect spoofing attacks. With the design of 51 sets of correlators in receiver channel for the first time, the method of estimating and correcting code phase delay error by virtual autocorrelation function is proposed, and the effective recovery of receiver navigation and positioning solution is realized by the extended Kalman filter. The spoofing scene tests of the Texas Spoofing Test Battery database show that the proposed anti-spoofing algorithm has a detection probability of more than 90% under the condition of a false alarm rate of 0.1%; the alarm time delay is less than 20 s and the time or position deviation is reduced from 600 m to less than 20 m, which has an excellent performance in detecting and mitigating spoofing attacks. The proposed anti-spoofing technique does not need external information and shows a wide range of potential applicability values for receivers equipped with anti-spoofing attack modules.

Satellite navigation spoofing has become a central issue of jamming technology research because of its serious threat and ability to conceal itself. Increasingly, targets are equipped with more robust GNSS/IMU systems and normalized innovation squared (NIS) is used to detect interference. Therefore, it is harder to implement covert trajectory spoofing on a GNSS/IMU system than a GNSS-only target. In practice, spoofing is needed to control unknown targets. Therefore, covert trajectory spoofing for GNSS/IMU targets is an important issue. Hence, using the information fusion of a GNSS/IMU system, the influence of spoofing on loosely coupled GNSS/IMU positioning is derived. To avoid ill-posed equations when introducing a measurement deviation, a Kalman gain matrix local regularization method is proposed to accurately determine the measurement deviation. To avoid triggering the NIS detection alarm, the range that enables the introduced measurement deviation to remain concealed is calculated. Then, a two-step trajectory guidance algorithm is proposed to quickly guide the target onto the spoofing trajectory. The simulation results show that the proposed trajectory spoofing algorithm can guide a loosely coupled GNSS/IMU target along a spoofing trajectory without triggering the NIS detection alarm. The proposed method can remain concealed and has good theorical and practical application value.

Once the spoofer has controlled the navigation system of unmanned aerial vehicle (UAV), it is hard to effectively control the error convergence to meet the threshold condition only by adjusting parameters of estimation if estimation of the spoofer on UAV has continuous observation error. Aiming at this problem, the influence of the spoofer's state estimation error on spoofing effect and error convergence conditions is theoretically analyzed, and an improved adaptively robust estimation algorithm suitable for steady-state linear quadratic estimator is proposed. It enables the spoofer's estimator to reliably estimate UAV status in real time, improves the robustness of the estimator in responding to observation errors, and accelerates the convergence time of error control. Simulation experiments show that the mean value of normalized innovation squared (NIS) is reduced by 88.5%, and the convergence time of NIS value is reduced by 76.3%, the convergence time of true trajectory error of UAV is reduced by 42.3%, the convergence time of estimated trajectory error of UAV is reduced by 67.4%, the convergence time of estimated trajectory error of the spoofer is reduced by 33.7%, and the convergence time of broadcast trajectory error of the spoofer is reduced by 54.8% when the improved algorithm is used. The improved algorithm can make UAV deviate from preset trajectory to spoofing trajectory more effectively and more subtly.