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.

A cooperative integrity monitoring (CIM) algorithm is proposed in this work. Under the CIM architecture, the algorithm can fully exploit the global navigation satellite system (GNSS) data and inter-vehicle measurements data to improve the detection and isolation of faulty measurements due to multipath or non line of sight (NLOS). Taking the advantages of cooperative scheme, a residual decomposition method is used to model the measurement errors into common and specific parts, a greedy search strategy is used to exclude the faulty measurements based on its sub-statistics. Simulation results show that CIM has better detection of GNSS fault than traditional receiver autonomous integrity monitoring (RAIM). Also, CIM is capable of detecting the faulty outliers in inter-vehicle measurements. The results indicate that CIM can be applied to many existing multi-sensor cooperative positioning algorithms.

Spaceborne global navigation satellite system reflectometry (GNSS-R) techniques have been developed for sea surface wind speed retrieval in recent years. In order to utilize both the leading edge slope (LES) and normalized bistatic radar cross-section (NBRCS), the minimum variance estimator (MVE) is used in the cyclone GNSS (CYGNSS) algorithm for wind retrieval. However, due to the high correlation of two observables, the root mean square error (RMSE) of the MVE estimated winds is not improved significantly. In this article, a new method by combining retrievals from delay-Doppler map (DDM) observables based on particle swarm optimization (PSO) is proposed. LES and NBRCS observables from CYGNSS V2.1 products are used, and then wind retrievals from them are combined by PSO. In order to validate the performance, European Center for Medium-Range Weather Forecasts (ECMWF) and cross-calibrated multi-platform (CCMP) ocean surface wind vector analysis product 10-m ocean surface wind products are used as ground truth. The results show that, when using ECMWF winds, the RMSE of MVE retrievals is 2.21 m/s, while that of PSO is 1.95 m/s: an improvement of 12%; when using CCMP winds, the RMSE of MVE retrievals is 2.15 m/s, while that of PSO is 1.92 m/s: an improvement of 11%. Therefore, we conclude that the PSO algorithm is an improvement on the state-of-the-art MVE GNSS-R-based wind speed retrieval techniques. However, the PSO-based wind retrievals show the dependence on the GPS block type and the CYGNSS satellite identifier that the MVE-based techniques suffer from.