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.INDEX TERMS Innovation rate optimization, robust estimation, spoofing detection, tightly coupled GNSS/INS integration.
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.
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