Simulated Global Navigation Satellite System Research Articles

High-Precision Digital Clock Steering Method Based on Discrete Σ-Δ Modulation for GNSS

A high-precision time reference is fundamental to the positioning, navigation, and timing (PNT) of global navigation satellite systems (GNSS). The precision of clock steering determines the accuracy of practical applications that rely on the time–frequency reference. With the invention of direct digital synthesizer (DDS) technology, digital clock steering (DCS) has gradually become a mainstream technology. However, the key factor limiting DCS accuracy is the system quantization noise, which leads to a low frequency and phase adjustment accuracy. Here we propose a DCS method based on Σ-Δ modulation to address the issue of low resolution of DAC through shaping the quantization noise. A simulated GNSS time–frequency reference system experimental platform is constructed to validate the effectiveness of the proposed method. The experimental results demonstrate that this method achieves a phase adjustment accuracy of 0.48 ps and a frequency adjustment accuracy better than 0.48 pHz, which is two orders of magnitude higher than that of existing GNSS time–frequency reference systems. Thus, the proposed method offers a significant improvement in time–frequency reference systems, leading to better performance, reliability, and accuracy in a wide range of practical applications.

A Novel Machine Learning-Based ANFIS Calibrated RISS/GNSS Integration for Improved Navigation in Urban Environments.

Autonomous vehicles (AVs) require accurate navigation, but the reliability of Global Navigation Satellite Systems (GNSS) can be degraded by signal blockage and multipath interference in urban areas. Therefore, a navigation system that integrates a calibrated Reduced Inertial Sensors System (RISS) with GNSS is proposed. The system employs a machine-learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) as a novel calibration technique to improve the accuracy and reliability of the RISS. The ANFIS-based RISS/GNSS integration provides a more precise navigation solution in such environments. The effectiveness of the proposed integration scheme was validated by conducting tests using real road trajectory and simulated GNSS outages ranging from 50 to 150 s. The results demonstrate a significant improvement in 2D position Root Mean Square Error (RMSE) of 43.8% and 28% compared to the traditional RISS/GNSS and the frequency modulated continuous wave (FMCW) Radar (Rad)/RISS/GNSS integrated navigation systems, respectively. Moreover, an improvement of 47.5% and 23.4% in 2D position maximum errors is achieved compared to the RISS/GNSS and the Rad/RISS/GNSS integrated navigation systems, respectively. These results reveal significant improvements in positioning accuracy, which is essential for safe and efficient navigation. The long-term stability of the proposed system makes it suitable for various navigation applications, particularly those requiring continuous and precise positioning information. The ANFIS-based approach used in the proposed system is extendable to other low-end IMUs, making it an attractive option for a wide range of applications.

ENHANCING URBAN VEHICULAR NAVIGATION: IMPROVING CLASSICAL TOPOLOGICAL MAP MATCHING THROUGH RAY-CASTING

Abstract. Navigation is of paramount importance for land vehicles as it enables efficient and accurate movement from one location to another. Whether it is for personal navigation, commercial transportation, or emergency services, reliable navigation systems play a crucial role in ensuring safety, optimizing routes, and enhancing overall operational efficiency. This paper presents the integration of classical Topological Map Matching (TMM) with the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS), addressing the limitations of relying solely on road centerlines. A novel solution is proposed, leveraging the ray-casting algorithm to determine the predicted position’s area and employing a two-stage kinematic update process for enhanced positioning accuracy. The solution’s efficacy is evaluated through tests conducted on simulated GNSS outages within a road experiment conducted in the City of Toronto, demonstrating substantial improvements compared to the classical TMM approach. Notably, the proposed method achieved a considerable 82.33% reduction in RMS positioning error and a 33.71% improvement in maximum positioning error during the longest GNSS outage. By overcoming the limitations of classical TMM algorithms, this research contributes to the advancement of navigation and tracking systems, with future work focusing on practical implementations and optimization for diverse navigation scenarios.

RADAR/INS TIGHTLY-COUPLED INTEGRATION FOR LAND VEHICLE NAVIGATION

Abstract. Multisensor systems are essential for autonomous navigation applications to achieve reliable accuracy. Integrating the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) is the most common integration scheme. However, this integration is unreliable in different scenarios since the GNSS signal may deteriorate in downtown areas or suffer from a blockage in underground and indoor areas. Therefore, other sensors are integrated with INS to compensate for GNSS outages. This paper proposes a novel algorithm for radar/INS tightly-coupled integration for autonomous navigation applications. This algorithm is applied in multiple steps. Radar data analysis is the first and most crucial step to remove the noisy data and the outliers and keep the useful objects. Then, data association is done to match the detected objects between radar frames. The tightly-coupled integration is performed at the measurement level through an Extended Kalman Filter (EKF), where the distance between the INS and the detected objects can be predicted from the INS and measured from the radar. Real data was collected from four Frequency Modulated Continuous Wave (FMCW) radar units in Calgary's suburban areas and Toronto's downtown area. The proposed algorithm was tested and assessed by introducing simulated GNSS single outages with different durations. The results show an enhancement in the vehicle's position by about 94% to 96%.

Numerical Analysis of GNSS Signal Outage Effect on EOPs Solutions Using Tightly Coupled GNSS/IMU Integration: A Simulated Case Study in Sweden.

The absence of a reliable Global Navigation Satellite System (GNSS) signal leads to degraded position robustness in standalone receivers. To address this issue, integrating GNSS with inertial measurement units (IMUs) can improve positioning accuracy. This article analyzes the performance of tightly coupled GNSS/IMU integration, specifically the forward Kalman filter and smoothing algorithm, using both single and network GNSS stations and the post-processed kinematic (PPK) method. Additionally, the impact of simulated GNSS signal outage on exterior orientation parameters (EOPs) solutions is investigated. Results demonstrate that the smoothing algorithm enhances positioning uncertainty (RMSE) for north, east, and heading by approximately 17-43% (e.g., it improves north RMSE from 51 mm to a range of 42 mm, representing a 17% improvement). Orientation uncertainty is reduced by about 60% for roll, pitch, and heading. Moreover, the algorithm mitigates the effects of GNSS signal outage, improving position uncertainty by up to 95% and orientation uncertainty by up to 60% using the smoothing algorithm instead of the forward Kalman filter for signal outages up to 180 s.

Radar/INS Integration and Map Matching for Land Vehicle Navigation in Urban Environments.

Autonomous navigation requires multi-sensor fusion to achieve a high level of accuracy in different environments. Global navigation satellite system (GNSS) receivers are the main components in most navigation systems. However, GNSS signals are subject to blockage and multipath effects in challenging areas, e.g., tunnels, underground parking, and downtown or urban areas. Therefore, different sensors, such as inertial navigation systems (INSs) and radar, can be used to compensate for GNSS signal deterioration and to meet continuity requirements. In this paper, a novel algorithm was applied to improve land vehicle navigation in GNSS-challenging environments through radar/INS integration and map matching. Four radar units were utilized in this work. Two units were used to estimate the vehicle's forward velocity, and the four units were used together to estimate the vehicle's position. The integrated solution was estimated in two steps. First, the radar solution was fused with an INS through an extended Kalman filter (EKF). Second, map matching was used to correct the radar/INS integrated position using OpenStreetMap (OSM). The developed algorithm was evaluated using real data collected in Calgary's urban area and downtown Toronto. The results show the efficiency of the proposed method, which had a horizontal position RMS error percentage of less than 1% of the distance traveled for three minutes of a simulated GNSS outage.

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter.

High-precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch-Tung-Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF-RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF-RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF-RTS algorithm is more robust and provides more accurate localization.

Low-Cost GNSS Simulators With Wireless Clock Synchronization for Indoor Positioning

In regions where global navigation satellite systems (GNSS) signals are unavailable, such as underground areas and tunnels, GNSS simulators can be deployed for transmitting simulated GNSS signals. Then, a GNSS receiver in the simulator coverage outputs the position based on the received GNSS signals (e.g., Global Positioning System (GPS) L1 signals in this study) transmitted by the corresponding simulator. This approach provides periodic position updates to GNSS users while deploying a small number of simulators without modifying the hardware and software of user receivers. However, the simulator clock should be synchronized to the GNSS satellite clock to generate almost identical signals to the live-sky GNSS signals, which is necessary for seamless indoor and outdoor positioning handover. The conventional clock synchronization method based on the wired connection between each simulator and an outdoor GNSS antenna causes practical difficulty and increases the cost of deploying the simulators. This study proposes a wireless clock synchronization method based on a private time server and time delay calibration. Additionally, we derived the constraints for determining the optimal simulator coverage and separation between adjacent simulators. The positioning performance of the proposed GPS simulator-based indoor positioning system was demonstrated in the underground testbed for a driving vehicle with a GPS receiver and a pedestrian with a smartphone. The average position errors were 3.7 m for the vehicle and 9.6 m for the pedestrian during the field tests with successful indoor and outdoor positioning handovers. Since those errors are within the coverage of each deployed simulator, it is confirmed that the proposed system with wireless clock synchronization can effectively provide periodic position updates to users where live-sky GNSS signals are unavailable.

A real-time algorithm for continuous navigation in intelligent transportation systems using LiDAR-Gyroscope-Odometer integration

Abstract Real-time positioning in suburban and urban environments has been a challenging task for many Intelligent Transportation Systems (ITS) applications. In these environments, positioning using Global Navigation Satellite Systems (GNSS) cannot provide continuous solutions due to the blockage of signals in harsh scenarios. Consequently, it is intrinsic to have an independent positioning system capable of providing accurate and reliable positional solutions over GNSS outages. This study exploits the integration of Light Detection and Ranging (LiDAR), gyroscope, and odometer sensors, and a novel real-time algorithm is proposed for this integration. Real field data, collected by a moving land vehicle, is used to test the presented algorithm. Three simulated GNSS outages are introduced in the trajectory such that each outage lasts for five minutes. The results show that using the proposed algorithm can achieve a promising navigation performance in urban environments. In addition, it is shown that the denser environments, that existed over the second and third outages, can provide better positioning accuracies as more features are extracted. The horizontal errors over the first outage, with less density of surroundings, reached 7.74 m (0.43%) error with a mean value of 3.15 m. Moreover, the horizontal errors in the denser environments over the second and third outages reached 4.97 m (0.28%) and 3.99 m (0.23%), with mean values of 2.25 m and 1.89 m, respectively.

GNSS Signal Processing Based Attitude Determination of Spinning Projectiles

This article presents a global navigation satellite system (GNSS) based attitude (pitch, roll, and heading) determination method for spinning objects by estimating the unit vector along the projectile nose and the antenna pointing vector that lies radially in the plane of rotation. A theoretical framework of a spinning platform is developed by exploiting the periodic variations of the received signal. The presented method provides a drift-free attitude determination of spinning projectiles directly in earth-centered earth-fixed reference. The scheme may find a good combination with low-cost small projectiles where the provision of advanced inertial sensors is constrained by cost and installation space. The proposed solution can be adopted for different degrees of accuracy and computational complexity. The performance guarantee for the proposed algorithm is established using simulated data and real GNSS data. For real satellite signals, the performance results are validated using an experimental setup having a roll rate of around 750 r/min.

Impact of the Earth Rotation Compensation on MEMS-IMU Preintegration of Factor Graph Optimization

In filtering-based global navigation satellite system (GNSS) / inertial navigation system (INS) integrated navigation systems, the precise INS mechanization has fully considered the impact of the Earth rotation, Coriolis acceleration, etc. However, most inertial measurement unit (IMU) preintegration models in factor graph optimization (FGO)-based integrated navigation systems are rough and ignore these factors. We propose an FGO-based GNSS/INS integrated navigation system to analyze and evaluate the impact of the Earth rotation on MEMS-IMU preintegration. The proposed FGO-based integration is based on a refined IMU preintegration, in which the Earth rotation is compensated. The proposed GNSS/INS integration is a sliding-window optimizer in which the GNSS positioning and the IMU preintegration are tightly fused within the FGO framework. The simulated GNSS outage in field tests, an effective method, is adopted to evaluate the IMU preintegration quantitatively. The results demonstrate that the refined IMU preintegration can achieve the same accuracy as the precise INS mechanization. In contrast, the rough IMU preintegration and INS mechanization without compensating for the Earth rotation yields notable accuracy degradation. When the GNSS-outage time is 60 seconds, the degradation can be 200% for the industrial-grade MEMS module and more than 10% for the consumer-grade MEMS chip. Besides, the degradation can be more significant if the GNSS-outage time is longer. The proposed FGO-based GNSS/INS integration ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/i2Nav-WHU/OB_GINS</uri> ) and the employed datasets ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/i2Nav-WHU/awesome-gins-datasets</uri> ) are open-sourced on GitHub.

Performance Assessment of BDS-3 PPP-B2b/INS Loosely Coupled Integration

The BeiDou global navigation satellite system (BDS-3) has been officially providing a real-time precise point positioning (PPP) augmentation service, known as the PPP-B2b service, since 2020. Decimeter-level positioning accuracy is expected to be achieved based on the PPP-B2b service. It shows great potential for global navigation satellite system (GNSS) real-time applications, including, for example, vehicle positioning on land. However, the application of the PPP-B2b service is still full of challenges in the urban environment because of GNSS signal blockage. The inertial navigation system (INS) is a popular technology which can provide continuous positions under GNSS challenging scenarios. In this study, we constructed a BDS-3 PPP-B2b/INS loosely coupled integration system for vehicle positioning and evaluated its performance through two automotive experiments. In the first experiment, four periods of 30 s GNSS outages were simulated to evaluate the performance of PPP-B2b/INS loosely coupled integration during GNSS outages. During the simulated GNSS outages, PPP-B2b positioning did not work. Nevertheless, PPP-B2b/INS loosely coupled integration provided continues solution through INS mechanization. The averaged positioning errors at the last epoch of outages were 300.6/498.0/41.0 cm for PPP-B2b/MEMS-IMU and 18.6/21.8/6.1 cm for PPP-B2b/Tactical-IMU loosely coupled integration, in the east, north and up directions, respectively. In the second experiment, we drove the land vehicle in a complex urban environment for 15 min. During this period, two GNSS signal interruptions occurred due to the occlusion of bridges, lasting 15 s and 5 s, respectively. The results show that the improvement of positioning accuracy in the east, north, and up components were 64.1%, 77.8%, and 73.8% respectively for PPP-B2b/MEMS-IMU loosely coupled integration, and 63.9%, 79.5%, and 74.4% respectively for PPP-B2b/Tactical-IMU loosely coupled integration, as compared to the positioning accuracy of PPP-B2b only.

IMPACT OF GNSS SIGNAL OUTAGE ON EOPS USING FORWARD KALMAN FILTER AND SMOOTHING ALGORITHM

Abstract. The global navigation satellite system (GNSS) has been playing the principal role in positioning applications in past decades. Position robustness degrades with a standalone receiver due to GNSS signal outage in mobile mapping systems. The GNSS and inertial measurement unit (IMU) integration is used to solve positioning degradation. This article studies the GNSS/IMU integration processing (i.e., forward Kalman filter (KF) and smoothing algorithm) using a single or a network of nearby GNSS reference stations. In addition, we analyze the impact of simulated GNSS signal outage on exterior orientation parameters (EOPs). The outcomes confirm that the smoothing algorithm works better than the forward KF and improves the accuracy for position and orientation in the case when there is no GNSS signal outage. Also, it improves the position and orientation accuracy by about 95% and 60% when there is a 180 second GNSS signal outage, respectively.

Roll Rate Estimation for Cylindrical Spinning Vehicle Based on Pseudorange Observations

Roll rate estimation is essential to attitude measurement. During the processing of baseband signals, it is complicated to measure the vehicle rolling attitude, based on the energy features of existing satellite signals. To measure the energy of received signals in real time, the data communication interface and storage unit of the receiving device must be customized. However, the customization will push up the cost of attitude sensors, and result in processing delays. To solve the problem, this paper measures the vehicle roll rate by the variation features of pseudorange observations, which are easy to acquire and highly applicable. Specifically, the roll rate of a cylindrical spinning vehicle was estimated, using Global Navigation Satellite System (GNSS) receiver with single side-mounted antenna. After analyzing the variation of pseudorange observations, the authors calculated the roll rate of the cylindrical spinning vehicle based on the variation rules. The proposed approach was verified through experiments with simulated GNSS signals. The accuracy of our approach was measured at different rotation speeds and translational speeds. The results show that our approach can accurately estimate the roll rate when the standard deviations of noise is less than 1m for pseudorange measurement.

A Batch Algorithm for GNSS Carrier Phase Cycle Slip Correction

Signal-phase measurements from global navigation satellite systems (GNSSs) have become an important tool for various remote sensing applications, including measuring ionosphere plasma content, atmospheric radio occultation, and water and ice reflectometry. In these types of scenarios, GNSS signals often experience harsh propagation conditions, such as low signal-to-noise ratios, multipath, and semicoherent scattering. These conditions, in turn, lead to the frequent occurrence of cycle slips, which manifests as persistent discrete changes in the bias of the carrier phase measurement. In order to effectively use the precise GNSS phase measurements under such conditions, we argue that a window of high-rate measurements must be used. In addition, we suggest that enforcing sparsity in the occurrence of detected cycle slips can aid in detection. We, therefore, develop a batch cycle-slip detection and estimation method that is effective and computationally tractable under harsh signal conditions. This work focuses in particular on strong ionosphere scintillation, which is among the most difficult scenarios for estimating cycle slips. We demonstrate the effectiveness of our method on both simulated and real GNSS scintillation datasets, showing around a 90% reduction of slips.

Sparse GLONASS Signal Acquisition Based on Compressive Sensing and Multiple Measurement Vectors

A sparse global navigation satellite system (GLONASS) signal acquisition method based on compressive sensing and multiple measurement vectors is proposed. The nonsparse GLONASS signal can be represented sparsely on our proposed dictionary which is designed based on the signal feature. Then, the GLONASS signal is sensed by a normalized orthogonal random matrix and acquired by the improved multiple measurement vectors acquisition algorithm. There are 10 cycles of pseudorandom codes in a navigation message, and these 10 pseudorandom codes have the same row sparse structure. So, the acquisition probability can be raised by row sparse features theoretically. A large number of simulated GLONASS signal experiments show that the acquisition probability increases with the increase in the measurement vector column dimension. Finally, the practical availability of the new method is verified by acquisition experiments with the real record GLONASS signal. The new method can reduce the storage space and energy loss of data transmission. We hope that the new method can be applied to field receivers that need to record and transmit navigation data for a long time.

Effects of GNSS Receiver Tuning on the PLL Tracking Jitter Estimation in the Presence of Ionospheric Scintillation

AbstractIonospheric scintillation is an interference characterized by rapid and random fluctuations in radio frequency signals when passing through irregularities in the ionosphere. It can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers, thus increasing positioning errors. Receivers with different tracking loop bandwidths and coherent integration times perform differently under scintillation. This study investigates the effects of GNSS receiver tracking loop tuning on scintillation monitoring and Phase Locked Loop (PLL) tracking jitter estimation using simulated GNSS data. The variation of carrier to noise density ratio (C/N0) under scintillation with different tracking loop settings is also studied. The results show that receiver tuning has a minor effect on scintillation indices calculation. The levels of C/N0 are also similar for different PLL bandwidths and integration times. Additionally, the tracking jitter is estimated by theoretical equations and verified using the relationship with the PLL discriminator output noise, which is calculated using the post‐correlation measurements. Novel approaches are further proposed to calculate 1‐s scintillation index, which enables to compute the tracking jitter at a rate of 1 s. It is found that 1‐s tracking jitter can successfully represent the signal fluctuations levels caused by scintillation. This work is valuable for developing scintillation sensitive tracking error models and is also of great significance for GNSS receiver design to mitigate scintillation effects.

Extracting Seasonal Signals in GNSS Coordinate Time Series via Weighted Nuclear Norm Minimization

Global Navigation Satellite System (GNSS) coordinate time series contains obvious seasonal signals, which mainly manifest as a superposition of annual and semi-annual oscillations. Accurate extraction of seasonal signals is of great importance for understanding various geophysical phenomena. In this paper, a Weighted Nuclear Norm Minimization (WNNM) is proposed to extract the seasonal signals from the GNSS coordinate time series. WNNM assigns different weights to different singular values that enable us to estimate an approximate low rank matrix from its noisy matrix. To address this issue, the low rank characteristics of the Hankel matrix induced by GNSS coordinate time series was investigated first, and then the WNNM is applied to extract the seasonal signals in the GNSS coordinate time series. Meanwhile, the residuals have been analyzed, obtaining the estimation of the uncertainty of velocity. To demonstrate the effectiveness of the proposed algorithm, a number of tests have been carried out on both simulated and real GNSS dataset. Experimental results indicate that the proposed scheme offers preferable performances compared with many state-of-the-art methods.

Integrated Precise Orbit Determination of Multi-GNSS and Large LEO Constellations

Global navigation satellite system (GNSS) orbits are traditionally determined by observation data of ground stations, which usually need even global distribution to ensure adequate observation geometry strength. However, good tracking geometry cannot be achieved for all GNSS satellites due to many factors, such as limited ground stations and special stationary characteristics for the geostationary Earth orbit (GEO) satellites in the BeiDou constellation. Fortunately, the onboard observations from low earth orbiters (LEO) can be an important supplement to overcome the weakness in tracking geometry. In this contribution, we perform large LEO constellation-augmented multi-GNSS precise orbit determination (POD) based on simulated GNSS observations. Six LEO constellations with different satellites numbers, orbit types, and altitudes, as well as global and regional ground networks, are designed to assess the influence of different tracking configurations on the integrated POD. Then, onboard and ground-based GNSS observations are simulated, without regard to the observations between LEO satellites and ground stations. The results show that compared with ground-based POD, a remarkable accuracy improvement of over 70% can be observed for all GNSS satellites when the entire LEO constellation is introduced. Particularly, BDS GEO satellites can obtain centimeter-level orbits, with the largest accuracy improvement being 98%. Compared with the 60-LEO and 66-LEO schemes, the 96-LEO scheme yields an improvement in orbit accuracy of about 1 cm for GEO satellites and 1 mm for other satellites because of the increase of LEO satellites, but leading to a steep rise in the computational time. In terms of the orbital types, the sun-synchronous-orbiting constellation can yield a better tracking geometry for GNSS satellites and a stronger augmentation than the polar-orbiting constellation. As for the LEO altitude, there are almost no large-orbit accuracy differences among the 600, 1000, and 1400 km schemes except for BDS GEO satellites. Furthermore, the GNSS orbit is found to have less dependence on ground stations when incorporating a large number of LEO. The orbit accuracy of the integrated POD with 8 global stations is almost comparable to the result of integrated POD with a denser global network of 65 stations. In addition, we also present an analysis concerning the integrated POD with a partial LEO constellation. The result demonstrates that introducing part of a LEO constellation can be an effective way to balance the conflict between the orbit accuracy and computational efficiency.

A New Threat for Pseudorange-Based RAIM: Adversarial Attacks on GNSS Positioning

Global Navigation Satellite System (GNSS) signals are very vulnerable to spoofing due to the low power level and opening service mode. Although pseudorange-based Receiver Autonomous Integrity Monitoring (RAIM) method performances effectively in spoofing detection and exclusion through a consistency check method, it might still suffer from threats which are deliberately designed. Besides an exhaustive research on the defense method, a deep understanding of the possible attack modes of the spoofing methods is also crucial for positioning security. This paper proposes a new threat for pseudorange-based RAIM, named adversarial attacks on GNSS positioning, which can mislead the victim receiver deducing any targeted position within a certain range without triggering the RAIM alarm. First, the adversarial attack is formulated as an optimization problem that minimizes the difference between the misled GNSS positioning results and the targeted position with RAIM missing detection. Then, the Fast Gradient Sign Method (FGSM) is applied to solve the optimization problem and obtain the adversarial examples for GNSS spoofing. Simulated and real GNSS data experiments are designed to verify the threat range and efficiency of the adversarial attacks on GNSS positioning.