Global Navigation Satellite System (GNSS) signal is vulnerable and easily interfered by spoofing because of its opening signal structure and weak signal power. Signal quality monitoring (SQM) technique has been shown to be viable to detect spoofing attacks on GNSS signals. However, the effectiveness of spoofing detection employing a single SQM metric alone is limited and conditional upon the features extracted of that specific SQM metric. The complementary features amongst various SQM metrics can be exploited to implement joint detection to overcome the deficiency of the individual SQM metric. Motivated by this idea, this work investigates the multi-metric joint detection technique which combines various SQM metrics into a composite SQM metric to detect spoofing attacks. This work proposes two combination strategies, namely amplitude combination mode (AmpM) and probability of false alarm combination mode (PfaM). The overall performance of different metric combinations was verified using simulations and the Texas Spoofing Test Battery (TEXBAT) dataset. Results show that the PfaM detector outperforms all single SQM metric detectors under various scenarios.

The fifth-generation (5G) network can be used jointly with a global navigation satellite system (GNSS) to help relieve the problem of having few visible satellites in urban environments and achieve highly accurate localization. The 5G base stations (BSs) can provide range and angle measurements at a much higher rate than GNSS. However, this benefit has not been fully employed by existing GNSS-5G hybrid methods. In addition, the near–far effect of 5G BS causes a large dynamic range of the measurement noise uncertainty. This challenge has not been well dealt with by the existing adaptive noise estimation methods, resulting in the decline of positioning accuracy and system convergence. This work proposes a multiple-rate adaptive Kalman filter (MRAKF) for GNSS-5G hybrid positioning with a hybrid sequential fusing scheme. It supports the fusion of GNSS and 5G measurements at different data rates to better employ the high-rate 5G measurements. A distance-dependent model is derived to quantitatively characterize the effect of BS-user equipment (UE) distance over the measurement noise covariance, leading to a proactive measurement uncertainty prediction algorithm to adaptively adjust the observation noise covariance matrix. Furthermore, a switchover strategy is proposed to switch its positioning mode to avoid the impact of variation of available line of sight (LOS) BSs. Both simulations and a real driving test were carried out. Results show that the proposed MRAKF method significantly improves the positioning accuracy compared with the other four adaptive noise estimation methods and the standard EKF. It requires the lowest computation complexity among all adaptive methods in this work.

Signal quality monitoring (SQM) has been proven to be a simple and effective means for the detection of spoofing attacks for global navigation satellite system (GNSS) instruments. Some prevalent SQM metrics include Ratio and Delta metrics. However, such SQM metrics have inherent defects, such as limited spoofing detection accuracy and low robustness due to the false alarms caused by environmental effects, such as multipath. The construction of conventional SQM metrics is mainly based on the in-phase correlator outputs in the tracking loop of a GNSS instrument. It is known that the interaction between spoofing and authentic signals will cause leakage of correlation energy to the quadrature channel. This article proposes a new SQM metric using this abnormal quadrature channel energy as the primary indicator. A spoofing and multipath discrimination scheme based on intersatellite cross-check is further developed. The detection performance of the quadrature channel-based SQM metric is verified in spoofing and multipath scenarios using the well-known Texas Spoofing Test Battery (TEXBAT) dataset and real multipath data collected in Beihang University, respectively. Results demonstrate that the proposed method achieves a higher than 95% detection rate for the TEXBAT Scenario 2 with P _fa of 10 ^-2 and an averaging time of 4 s, showing better detection sensitivity and robustness compared with conventional I-channel SQM metrics.

Global navigation satellite system (GNSS) spoofing attacks pose severe threats to position security. Since the fifth generation (5G) signals are unaffected by GNSS spoofing attacks, they have the potential to provide external assistance to detect GNSS spoofing threats and recover the user equipment (UE)’s authentic position. It can be easily achieved when multiple 5G base stations (BSs) are accessible, as the true position can be directly calculated by using 5G measurements alone. But for a common situation where only one 5G BS is available, this problem would be difficult. This work aims to detect GNSS spoofing attacks and help the UE recover its authentic position when only one BS is available. First, we extend the innovation-based anomaly detection concept to the GNSS-5G hybrid positioning system for spoofing detection. Then, the fusion extended Kalman Filter (EKF) output model under spoofing attacks is derived and proved mathematically. Based on this, a three-stage authentic position estimation algorithm is proposed. Both simulations and a real field test were implemented. Results illustrate that our method can directly rebuild the actual location of the UE from the spoofed GNSS-5G hybrid positioning result with only one 5G BS.