Using the vertical total electronic content (VTEC) value of the empirical ionospheric model to compensate for the deficiency of ground-based global navigation satellite system (GNSS) observation is an effective method to improve the accuracy and reliability of ionospheric modeling in the ocean and polar regions lacking ground-based GNSS observation stations. In the research of using empirical model virtual observations to enhance GNSS for ionospheric modeling, the empirical model error is usually taken as a fixed value for empirical weight determination when the empirical model and GNSS VTEC are weighted, and the variation of empirical model error with time and space is ignored. In this article, a GNSS ionospheric modeling method enhanced by international reference ionosphere (IRI)-2020 virtual observations based on Helmert variance component estimation (HVCE) is proposed. The weights of GNSS and IRI model VTEC are dynamically determined by HVCE. The models obtained from the GNSS ionospheric modeling enhanced by IRI-2020 model virtual observations using empirical weighting and Helmert variance component weighting methods are defined as GIP and GIH, respectively. Taking the Arctic region as an example, the Fengyun-3 (FY-3) VTEC is used as reference value to verify the modeling results from the 79th to 90th days of 2023. The results show that the mean absolute error (MAE), root mean squares (RMSs), and standard deviation (STD) of the GIH model are all smaller than those of the GIP, decreasing by 0.20 TECU, 0.30 TECU, and 0.24 TECU, respectively, which indicates that after weighting with the HVCE, the accuracy of ionospheric modeling in the Arctic can be improved. The analysis of ionosphere TEC accuracy at different longitudes, latitudes, and local times shows that GIH has a greater improvement in accuracy compared to GIP in the data blank areas of longitude 60 degrees E-180 degrees E, latitude greater than 84 degrees, and local time 12-18.

Signal Quality Monitoring (SQM) methods have been proved to be an effective means of GNSS spoofing detection. Based on the characteristics of the Q-channel outputs, we propose an innovative Quadrature(Q)channel Correlation Signal Quality Monitoring (Q-CSQM) metric. The core of the proposed method is to identify the noise difference in the received signals by monitoring the Q-channel early(E) and late(L) correlator outputs. In dataset (DS) 2-8 of the Texas Spoofing Test Battery (TEXBAT) and self-collected spoofing datasets Multi-Antenna Spoofing Dataset(MASD) 1-4, the proposed method achieves an average detection probability exceeding 97% with a false alarm rate of 1% and a detection window of 1 s. Additionally, in DS 4, it maintains an average detection probability of 95% across all channels, demonstrating strong robustness. This method delivers excellent detection performance with a relatively small number of correlators and offers a new perspective for spoofing detection based on Q-channel outputs.

Doppler positioning based on Low Earth Orbit (LEO) communication satellite signals with abundant frequency resources and high power has been shown to be a reliable alternative for positioning and navigation in GNSS-denied environments. However, Doppler positioning encounters the technical bottleneck of low accuracy because of the influence of several error factors, including an unknown precise satellite ephemeris and clock information, and the absence of appropriate weighting optimization schemes for various satellite observations. To significantly diminish the performance degradation caused by multiple errors, we creatively propose a combined Doppler positioning method based on semi-parametric regression model with additional system parameters. The core lies in adding an equivalent clock error parameter to compensate for primary modeled errors, including along-track orbit and clock ambiguity errors, and introducing non-parametric components to mitigate unmodeled errors, including radial and cross-track orbit errors. Moreover, a new combined weighting scheme that takes Doppler position dilution of precision into account was designed to allocate the weights of Iridium NEXT/Orbcomm observations rationally and optimize the spatial geometry configuration effectively. Additionally, the extraction accuracy of the Doppler measurements was improved using the chip-z transform algorithm. Comparative experiments demonstrated the effectiveness of the proposed method, yielding a three-dimensional positioning root mean square error of 34.43 m and a convergence time of 140 s. The method successfully overcomes the performance loss caused by multiple errors, presenting a potentially viable positioning solution for elastic PNT systems.

Multiple epoch stationary positioning can be achieved using signals of opportunity (SOPs) from low-Earth-orbit (LEO) satellites when a global navigation satellite system (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 the 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 (IDCs) using dual-frequency carrier phase measurements. Third, an adaptive regularized least squares (ARLSs) algorithm is proposed to estimate the satellite state and provide satellite position corrections (SPCs). 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 3-D positioning error of less than 5 m and a 3-D 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.

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.

Positioning technology based on signals of opportunity (SOPs) of low-earth-orbit (LEO) satellites has become an effective global positioning backup in GNSS denial environments. Currently, Doppler measurements are widely used because they are easy to obtain. However, the Doppler positioning has poor accuracy and stability, which is mainly due to the low Doppler measurement accuracy and the complex system errors caused by the satellite position and velocity errors of the two-line element (TLE) and the simplified general perturbation 4 (SGP4) model. Therefore, we first design a carrier tracking loop based on squaring and code phase assistance (S-CPA) for Orbcomm SOPs to obtain accurate carrier phase measurements under lower carrier-to-noise ratio (CNR) conditions. Second, we analyze the observability of main system errors in the carrier phase observation model. Subsequently, a carrier phase positioning algorithm with additional system parameters is proposed to reduce the influence of system errors. Additionally, three typical epoch selection schemes are considered for the multi-epoch positioning system. Simulation results show that the CNR threshold of the S-CPA loop is approximately 10 dB Hz lower than that of the conventional Costas loop. Experimental results show a three-dimensional (3D) positioning root mean squared error (RMSE) of 77.5 m; the positioning accuracy and stability of the proposed algorithm are 48% and 45% higher than that of the Doppler positioning, respectively.

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 (VTL) structure. This technique effectively utilizes the advantages of the VTL structure to construct equivalent noise bandwidth (ENB) detection statistics to detect spoofing attacks. With the design of 51 sets of correlators in the receiver channel for the first time, the method of estimating and correcting code phase delay error by virtual autocorrelation function (VACF) is proposed, and the effective recovery of receiver navigation and positioning solution is realized by the extended Kalman filter (EKF). The spoofing scene tests of the Texas Spoofing Test Battery (TEXBAT) 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.

欺骗式干扰对卫星导航接收机的影响是多层次的,现已成为接收机用户的一大主要威胁.近些年,学者们提出了许多抗欺骗技术.针对牵引式欺骗攻击,设计了一种信号处理层的抗欺骗技术,提出了基于载噪比的欺骗检测算法.该算法基于假设检验方法,使用信号载噪比为检测统计量,建立了检测模型,实现了欺骗信号检测.仿真实验和实测数据验证了该算法的有效性.实验结果表明,该方法能成功检测大功率优势欺骗干扰和小功率优势欺骗干扰,在虚警率为1×10-3时,检测概率分别达到95％和80％以上.该算法无需外部设备,可低成本地应用到现有的接收机.但对于功率匹配的欺骗干扰,该方法不再可靠,需结合其他的欺骗检测算法来联合检测.

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.

针对传统的新息抗差估计欺骗检测算法对缓慢增长的斜坡式欺骗检测时间较长甚至检测不敏感等问题,提出了一种GNSS/INS紧组合的新息优化抗差估计欺骗检测算法.所提出的算法对缓慢增长的斜坡式的新息检测量进行了优化,结合抗差估计自适应调整增益矩阵并合理选择"检测窗口",进一步提高了对缓慢增长的斜坡式欺骗干扰的检测效率和检测性能.仿真结果表明,在检测单通道0.1 m/s缓慢增长的斜坡式欺骗时,所提算法检测时间较新息抗差估计欺骗检测算法平均缩短了30％以上,漏检率为0;在检测多通道时,检测时间平均缩短了30％,漏检率为0,虚警率平均降低了18.5％.所提算法在检测缓慢增长的斜坡式欺骗干扰时,具有检测快、漏检率和虚警率低的优势,对无人机应用领域具有重要意义.