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.

Compared with color or grayscale images, hyperspectral images deliver more informative representation of ground objects and enhance the performance of many recognition and classification applications. However, hyperspectral images are normally corrupted by various types of noises, which have a serious impact on the subsequent image processing tasks. In this paper, we propose a novel hyperspectral image denoising method based on tucker decomposition to model the nonlocal similarity across the spatial domain and global similarity along the spectral domain. In this method, 3-D full band patches extracted from a hyperspectral image are grouped to form a third-order tensor by utilizing the nonlocal similarity in a proper window size. In this way, the task of image denoising is transformed into a high-order tensor approximation problem, which can be solved by nonnegative tucker decomposition. Instead of a traditional alternative least square based tucker decomposition, we propose a hierarchical least square based nonnegative tucker decomposition method to reduce the computational cost and improve the denoising effect. In addition, an iterative denoising strategy is adopted to achieve better denoising outcome in practice. Experimental results on three datasets show that the proposed method outperforms several state-of-the-art methods and significantly enhances the quality of the corrupted hyperspectral image.

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.

Global navigation satellite system (GNSS) signals have open structure and weak power, which is vulnerable to spoofing attacks. The Innovation-based detection technique has been proved effective for spoofing detection in an integrated navigation system. However, it employs spoofed priori estimate of pseudorange to construct innovation, showing shortcomings like limited detection probability for slowly-varying spoofing attacks, long time to alarm (TTA), and high false alarm probability. This work proposes an enhanced innovation-based spoofing detector via multiple-epoch inertial navigation sensor (INS) prediction. An additional INS unit is added, which is corrected by extended Kalman Filter (EKF) every ${N}$ epoch. Its output is used to replace the spoofed priori estimate in the EKF and then construct an improved innovation. Compared with the conventional innovation, the improved one can be immune to spoofing during multiple epochs and accumulate more abnormal energy due to spoofing attacks. In addition, an innovation bias mitigation method is presented, which exploits the mean of innovation of previous epochs to predict the immediate innovation bias and thus reduces the probability of false alarm. The overall spoofing detection performance is evaluated using both simulation data and hardware-based experiment data collected in Beihang university. A driving test was also carried out to verify its performance in complex urban conditions. Results show that the proposed detector significantly improves the detection performance under slowly-varying spoofing attacks and reduces the probability of false alarm compared with the conventional detector.

Hybrid positioning methods fusing Global Navigation Satellite System (GNSS) and the fifth generation (5G) measurements are receiving growing interest. These methods can address the limitations of individual GNSS positioning techniques and improve the security and reliability of positioning services. Particularly, 5G signals are impervious to GNSS spoofing attacks and have the potential to assist GNSS user equipment (UE) in detecting hazardous spoofing attacks. However, the conventional innovation-based fault detection scheme in a GNSS-5G hybrid system has a high false alarm probability due to UE maneuvers. In this paper, a new type of spoofing test statistic has been constructed by modifying the innovation in the conventional detection method. It utilizes GNSS single-point positioning results to predict the current 5G measurements, instead of the EKF-based predicted measurements. The difference between the newly predicted 5G measurements and the actual 5G measurements is used to construct a new type of innovation for spoofing detection. Simulations and two real driving tests were carried out, and the results indicate that the proposed method significantly mitigates the false detection problem due to UE’s maneuvers while enhancing the spoofing detection capability.