Global navigation satellite system (GNSS) spoofing causes the victim receiver to deduce false positioning and timing data; this notably threatens navigational safety. Thus, anti-spoofing techniques that improve the reliability of GNSS systems, for which interference detection is critical, are essential. Based on the distortion of tracking loop correlation function symmetry of the target receiver caused by gradual adjustment of induced spoofing signals, we proposed a new induced spoofing detection method that uses the weighted second-order central moment (WSCM) difference in the time-domain transient response of multiple correlators of the left and right peaks to obtain the test statistic, theoretically proving that the test statistic follows Gaussian distribution. The Neyman-Pearson hypothesis test method is used to determine the optimal test threshold and determine whether the receiver is being spoofed. The proposed WSCM-based method for spoofing detection was compared with three conventional methods in Scenarios 4 and 7 of the Texas Spoofing Test Battery database, showing that the detection probability of the proposed method is at least 24.15% higher at a false alarm rate of 10% and is more advantageous at lower false alarm rates and the alert time is shortened by at least 30 seconds, enabling at least a 20% faster detection efficiency. The proposed method overcomes the problem of existing methods, which are associated with difficulties in capturing the subtle time-varying effects of the relative carrier phase between the spoofing and authentic signals; thus, it provides excellent detection accuracy and effectiveness, showing broad potential applicability in GNSS spoofing detection.

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.

The spoofing detection algorithm for a global navigation satellite system/inertial navigation system (GNSS/INS) integrated navigation system based on the innovation rate and robust estimation has limitations such as extensive or invalid detection times, high missed detection rates, and false alarm rates. This study addresses these limitations by proposing a tightly coupled GNSS/INS integration spoofing detection algorithm based on innovation rate optimization and robust estimation. The proposed algorithm improved the normalized innovation of a small step or slow-growing ramp, thereby optimizing its innovation rate test statistics. The proposed approach also reduces the spoofing effect on the innovation rate by adaptively adjusting a gain matrix using robust estimation, thus improving the detection ability further. The simulation results show that the detection time of the proposed algorithm is reduced by 51.9% on average when dealing with small step or slow-growing ramp spoofing. Moreover, the missed detection rate decreases by 58% on average, and the false alarm rate remains at approximately zero. The proposed algorithm is suitable for spoofing detection in unmanned aerial vehicle applications of GNSS/INS integrated navigation systems with the advantages of fast detection and good performance.

Multiple epoch stationary positioning can be achieved using signals of opportunity (SOPs) from low-earth-orbit (LEO) satellites when GNSS is unavailable. Concurrently, the two-line element (TLE) and the simplified general perturbation 4 (SGP4) model can provide satellite positions with km-level errors for non-navigational LEO constellations. Although differential positioning can mitigate some system errors, long baselines reduce their temporal and spatial correlation, resulting in large residual system errors in differential measurements, which leads to inaccurate positioning results. First, based on SOPs of Orbcomm, we conduct a comprehensive analysis of the effect of major system errors on the differential range residual. Second, we introduce a polynomial fitting algorithm to obtain ionospheric delay corrections (IDC) using dual-frequency carrier phase measurements. Third, an adaptive regularized least squares (ARLS) algorithm is proposed to estimate the satellite state and provide satellite position corrections (SPC). Finally, differential carrier phase positioning utilizing IDC and SPC is implemented in real time. Experimental results demonstrate that the proposed algorithms can effectively smooth the variation of ionospheric delay over epochs and estimate position errors of Orbcomm satellites. Using two Orbcomm satellites, a final three-dimensional (3D) positioning error of less than 5 m and a 3D positioning root mean squared error (RMSE) of 10.8 m are achieved for the long-baseline scenario. Furthermore, the proposed differential positioning algorithm shows good robustness to the age of TLE files and does not require external ionospheric correction, enhancing its practicality and real-time performance.

To address the limitations of Global Navigation Satellite Systems (GNSSs), such as vulnerability to electromagnetic interference and weak ground signal power, signal of opportunity (SOP) provided by low Earth orbit (LEO) satellites can serve as a backup positioning method. By simulating a LEO constellation, the impact of satellite visibility, Doppler geometric dilution of precision (DGDOP), and positioning accuracy was explored. Considering positioning errors such as satellite clock drift rate, ionospheric delay rate, tropospheric delay rate, and Earth rotation effects, the instantaneous positioning performance with satellite orbital errors and satellite velocity errors of different magnitudes was simulated. The results show that satellite visibility and DGDOP are negatively correlated. In a typical atmospheric environment with orbital errors of 10 m and satellite velocity errors of 0.1 m/s, positioning accuracy within 30 m can be achieved. This confirms that Doppler-based positioning with LEO satellites can be used as a backup method for GNSSs.