Global Navigation Satellite System Research Articles

Estimation of Surface Water Level in Coal Mining Subsidence Area with GNSS RTK and GNSS-IR

Ground subsidence caused by underground coalmining result in the formation of ponding water on the ground surface. Monitoring the surface water level is crucial for studying the hydrologic cycle in mining areas. In this paper, we propose a combined technique using Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) and GNSS Interferometric Reflectometry (GNSS-IR) to estimate the surface water level in areas of ground subsidence caused by underground coal mining. GNSS RTK is used to measure the geodetic height of the GNSS antenna, which is then converted into the normal height using the local height anomaly model. GNSS-IR is employed to estimate the height from the water surface to the GNSS antenna (or, the reflector height). To enhance the accuracy of the reflector height estimation, a weighted average model has been developed. This model is based on the coefficient of determination of the signal fitted by the Lomb-Scargle spectrogram and can be utilized to combine the reflector height estimations derived from multiple GNSS system and band reflection signals. By subtracting the GNSS-IR reflector height from the GNSS RTK-based normal height, the proposed method-based surface water level estimation can be obtained. In an experimental campaign, a low-cost GNSS receiver was utilized for the collection of dual-frequency observations over a period of 60 days. The collected GNSS observations were used to test the method presented in this paper. The experimental campaign demonstrates a good agreement between the surface water level estimations derived from the method presented in this paper and the reference observations.

Sky-GVIO: Enhanced GNSS/INS/Vision Navigation with FCN-Based Sky Segmentation in Urban Canyon

Accurate, continuous, and reliable positioning is critical to achieving autonomous driving. However, in complex urban canyon environments, the vulnerability of stand-alone sensors and non-line-of-sight (NLOS) caused by high buildings, trees, and elevated structures seriously affect positioning results. To address these challenges, a sky-view image segmentation algorithm based on a fully convolutional network (FCN) is proposed for NLOS detection in global navigation satellite systems (GNSSs). Building upon this, a novel NLOS detection and mitigation algorithm (named S−NDM) uses a tightly coupled GNSS, inertial measurement units (IMUs), and a visual feature system called Sky−GVIO with the aim of achieving continuous and accurate positioning in urban canyon environments. Furthermore, the system combines single-point positioning (SPP) with real-time kinematic (RTK) methodologies to bolster its operational versatility and resilience. In urban canyon environments, the positioning performance of the S−NDM algorithm proposed in this paper is evaluated under different tightly coupled SPP−related and RTK−related models. The results exhibit that the Sky−GVIO system achieves meter-level accuracy under the SPP mode and sub-decimeter precision with RTK positioning, surpassing the performance of GNSS/INS/Vision frameworks devoid of S−NDM. Additionally, the sky-view image dataset, inclusive of training and evaluation subsets, has been made publicly accessible for scholarly exploration.

Effect of Trail Condition, Slope, and Direction of Extraction on Forwarding Performance: Insights from a Controlled Comparative Study

The performance of timber harvesting equipment is important for local operational planning and for making decisions on the way in which the forests should be opened up. However, there are many options used to extract timber, and there is a high variability in their performance. Forwarding is commonly used and became an attractive option for low-access forests. A controlled experiment was set up in this study to see how the configuration of the trails, characterized in terms of slope and surface condition, and the extraction direction (uphill or downhill) may affect the performance of forwarding operations. GNSS (Global Navigation Satellite System) data were collected at a rate of 5 s for five replications of moving empty downhill and uphill, respectively, on a dirt trail, measuring 250 m in length and having a slope of about 11%. The same experiment was run with the machine loaded at full capacity, then four replications with the machine loaded and unloaded moving downhill and uphill, respectively, were performed on a forest road resembling a rocky trail, which measured 390 in length and had a slope of about 4%. GNSS data were used to extract the moving speed for all the tested conditions with the aim to compute the cycle time, and the payload volume estimate was used to estimate efficiency, and productivity, depending on extraction distance in a range of 50 to 1000 m. For the first trail, statistical comparison tests indicated significant differences in the speed of uphill and downhill movement, for both empty and loaded conditions, whereas for the second trail, there were no significant differences in speed. In addition, on the second trail, the sustained speed was almost double. These were reflected sharply in the cycle time, efficiency, and productivity, depending on extraction distance and trail condition. These findings are important for decision making on local operational planning and forest opening up.

Mitigating ionospheric scintillation effects on RTK-based long-span bridge deformation monitoring

Ionospheric scintillation presents significant challenges to the application of Global Navigation Satellite System (GNSS) for positioning and bridge deformation monitoring in low-latitude region. This study aims to develop a GNSS positioning model to improve long-span bridge deformation monitoring performance under ionospheric scintillation. We propose a new Rate of Total Electron Content (ROT) calculation method using undifferenced estimated ionosphere delays for identifying ionospheric scintillation. Then, we construct an undifferenced and uncombined (UDUC) ionosphere-refined Real-time Kinematic (RTK) model by replacing ionospheric spatial constraints with temporal constraints to mitigate the impact of ionospheric scintillation. RTK experiments are conducted on the South China Sea Bridge (SCSB), spanning 55 km. The results indicate that the new ROT calculation method can effectively identify observations affected by ionospheric scintillation, and the ionosphere-refined RTK model shows superior performance compared to ionosphere-float and ionosphere-weighted RTK model in terms of convergence time, Time to First Fix (TTFF), and positioning accuracy.

YOLO-RWY: A Novel Runway Detection Model for Vision-Based Autonomous Landing of Fixed-Wing Unmanned Aerial Vehicles

In scenarios where global navigation satellite systems (GNSSs) and radio navigation systems are denied, vision-based autonomous landing (VAL) for fixed-wing unmanned aerial vehicles (UAVs) becomes essential. Accurate and real-time runway detection in VAL is vital for providing precise positional and orientational guidance. However, existing research faces significant challenges, including insufficient accuracy, inadequate real-time performance, poor robustness, and high susceptibility to disturbances. To address these challenges, this paper introduces a novel single-stage, anchor-free, and decoupled vision-based runway detection framework, referred to as YOLO-RWY. First, an enhanced data augmentation (EDA) module is incorporated to perform various augmentations, enriching image diversity, and introducing perturbations that improve generalization and safety. Second, a large separable kernel attention (LSKA) module is integrated into the backbone structure to provide a lightweight attention mechanism with a broad receptive field, enhancing feature representation. Third, the neck structure is reorganized as a bidirectional feature pyramid network (BiFPN) module with skip connections and attention allocation, enabling efficient multi-scale and across-stage feature fusion. Finally, the regression loss and task-aligned learning (TAL) assigner are optimized using efficient intersection over union (EIoU) to improve localization evaluation, resulting in faster and more accurate convergence. Comprehensive experiments demonstrate that YOLO-RWY achieves AP50:95 scores of 0.760, 0.611, and 0.413 on synthetic, real nominal, and real edge test sets of the landing approach runway detection (LARD) dataset, respectively. Deployment experiments on an edge device show that YOLO-RWY achieves an inference speed of 154.4 FPS under FP32 quantization with an image size of 640. The results indicate that the proposed YOLO-RWY model possesses strong generalization and real-time capabilities, enabling accurate runway detection in complex and challenging visual environments, and providing support for the onboard VAL systems of fixed-wing UAVs.

Development status and challenges of anti-spoofing technology of GNSS/INS integrated navigation

The threat of spoofing interference has posed a severe challenge to the security application of Global Navigation Satellite System (GNSS). It is particularly urgent and critical to carry out in-depth defense research on spoofing interference. When combined with the inertial navigation system (INS), the GNSS/INS integrated navigation system offers distinct advantages in the field of anti-spoofing technology research, which has garnered significant attention in recent years. To summarize the current research achievements of GNSS/INS integrated navigation anti-spoofing technology, it is necessary to provide an overview of the three core technical aspects of spoofing attack principles and implementation strategies, spoofing detection, and spoofing mitigation. First, the principles and implementation strategies of spoofing interference attacks are introduced, and different classifications of spoofing interference attacks are given. Then, the performance characteristics and technical points of different spoofing detection and spoofing mitigation methods are compared and analyzed, and the shortcomings and challenges in the current development of GNSS/INS anti-spoofing technology are pointed out. Finally, based on the summary and shortcomings of the existing technology, a prospect for the future development of GNSS/INS integrated navigation anti-spoofing technology is discussed.

Creep on the Argentine Precordillera Décollement Following the 2015 Illapel, Chile, Earthquake: Implications for Andean Seismotectonics

AbstractThe Central and South‐Central Andes form a “two‐sided” mountain belt bounded by distinct zones of convergence in the forearc and backarc flanks. Previous geodetic interseismic deformation studies found that the forearc to backarc velocity field is better explained when elastic models allow reverse aseismic slip on the Andes eastern‐flank décollement faults. Here, we extend the earlier interpretation of interseismic motion and argue that normal aseismic creep of the Precordillera décollement is required to explain backarc Global Navigation Satellite System displacements during the co‐ and early postseismic phases of the 2015 Illapel, Chile, earthquake. This model significantly reduces the previously reported overlap between coseismic slip and afterslip on the megathrust of this earthquake, consistent with the expectation that these slip modes are spatially partitioned. These findings have direct implications for estimating recurrence interval and slip rate, and for probabilistic seismic hazard analysis on both sides of the orogen.

Diurnal Vegetation Moisture Cycle in the Amazon and Response to Water Stress

AbstractWater stress in the Amazon is exacerbated by rising temperatures and reduced moisture levels. However, understanding forest responses to increased aridity is hindered by limited in situ water potential observations in the Amazon. Remote sensing of water content has emerged as a promising metric. Vegetation Water Content (VWC) diurnal dynamics is hypothesized to reflect water stress responses. Conventional sensors' low sampling rates impede capturing and studying sub‐daily VWC dynamics. Leveraging Global Navigation Satellite System Reflectometry (GNSS‐R) with unprecedented sampling rates, this study reveals significant disparities in morning and evening VWCs in the Amazon, for example, by 1.1 and 1.0 kg/ during the wet and dry seasons of 2019. A strong correlation between VWC (the difference between evening and morning VWCs) and vapor pressure deficit is observed in Amazonian peatland. This highlights the potential of VWC from innovative remote sensing techniques in elucidating water stress dynamics in critical ecosystems.

GNSS/IMU/LO integration with a new LO error model and lateral constraint for navigation in urban areas

The quest for reliable vehicle navigation in urban environments has led the integration of Light Detection and Ranging (LiDAR) Odometry (LO) with Global Navigation Satellite Systems (GNSS) and Inertial Measurement Units (IMU). However, the performance of the integrated system is limited by a lack of accurate LO error modeling. In this paper, we propose a weighted GNSS/IMU/LO integration-based navigation system with a novel LO error model. The Squared Exponential Gaussian Progress Regression (SE-GPR) based LO error model is developed by considering the vehicle velocity and number of point cloud features. Based on error prediction for GNSS positioning and LO, a weighting strategy is designed for integration in an Extended Kalman Filter (EKF). Furthermore, error accumulation of the navigation state, especially in GNSS-challenging scenarios, is restrained by the LiDAR-Aided Lateral Constraint (LALC) and Non-Holonomic Constraint (NHC). An experiment was conducted in a deep urban area to test the proposed algorithm. The results show that the proposed algorithm delivers horizontal and three-dimensional (3D) positioning Root Mean Square Errors (RMSEs) of 3.669 m and 5.216 m, respectively. The corresponding accuracy improvements are 35.9% and 50.0% compared to the basic EKF based GNSS/IMU/LO integration.

GNSS spoofing detection based on frequency domain processing

Global Navigation Satellite System (GNSS) spoofing detection has attracted much attention in recent years to ensure security. However, most of the existing methods use the time domain feature and can be easily affected by interference, which limits the practical application. To solve this problem, the frequency domain processing approach is investigated and a novel spoofing detection method is proposed. The basic idea is to exploit the spoofing-induced feature by transforming the correlator output into the frequency domain. The proposed method has a simple implementation logic and requires only minor software upgrades to existing GNSS receivers. Simulation and experimental results are presented to verify the effectiveness of the proposed method. The results demonstrate that it exhibits superior detection performance and shows robustness to interference. Consequently, this method has great application value and can serve as a valuable complement to current detection methods.

Autonomous Underwater Vehicle Navigation Enhancement by Optimized Side-Scan Sonar Registration and Improved Post-Processing Model Based on Factor Graph Optimization

Autonomous Underwater Vehicles (AUVs) equipped with Side-Scan Sonar (SSS) play a critical role in seabed mapping, where precise navigation data are essential for mosaicking sonar images to delineate the seafloor’s topography and feature locations. However, the accuracy of AUV navigation, based on Strapdown Inertial Navigation System (SINS)/Doppler Velocity Log (DVL) systems, tends to degrade over long-term mapping, which compromises the quality of sonar image mosaics. This study addresses the challenge by introducing a post-processing navigation method for AUV SSS surveys, utilizing Factor Graph Optimization (FGO). Specifically, the method utilizes an improved Fourier-based image registration algorithm to generate more robust relative position measurements. Then, through the integration of these measurements with data from SINS, DVL, and surface Global Navigation Satellite System (GNSS) within the FGO framework, the approach notably enhances the accuracy of the complete trajectory for AUV missions. Finally, the proposed method has been validated through both the simulation and AUV marine experiments.

An optimized BP neural network for modeling zenith tropospheric delay in the Chinese mainland using coupled particle swarm and genetic algorithm

ABSTRACT Tropospheric delay influences high-precision navigation positioning and precipitable water vapor retrieval with the Global Navigation Satellite System (GNSS). Existing Zenith Tropospheric Delay (ZTD) models often struggle to accurately capture the non-linear variations in tropospheric delay. Therefore, this study employs the coupled Particle Swarm Optimization (PSO) algorithm with the Genetic Algorithm Back Propagation (GABP) neural network, combined with ERA5 reanalysis meteorological data, to develop an optimized model (PSO-GABP) for ZTD in the Chinese mainland. Nevertheless, ZTD data at the target point are obtained through four different methods: the integration method, model method, and GPT3 models at varying resolutions (EZTD_P, EZTD_S, GPT3_1, and GPT3_5). The analysis reveals the following: (1) The Root Mean Square (RMS) errors of the ZTD values obtained through these different methods are 1.86 cm, 3.42 cm, 3.99 cm, and 4.09 cm, respectively, when verified against GNSS_ZTD data from 2016. The optimized model yields ZTD values with the RMS error of 0.98 cm, 1.96 cm, 2.34 cm, and 2.36 cm, representing improvements of 47.3%, 42.7%, 41.4%, and 42.3% compared to the pre-optimization results. These improvements are significant; (2) The predictive capability of the constructed ZTD model is evaluated using GNSS_ZTD data from 2019 as a reference. The PSO_EZTD_P model demonstrates excellent accuracy and practicality in the Chinese mainland. As a result, the tropospheric delay optimization model based on the PSO-GABP neural network can provide valuable references for real-time GNSS navigation positioning and precipitable water vapor detection in the Chinese mainland.

Assessment of New Techniques for Measuring Volume in Large Wood Chip Piles

Our work aimed to compare the chip pile volumes calculated by laser ground scanning, UAV technology, and laser ground measurement and also to determine the accuracy, speed, and economic efficiency of each method. The large chip pile was measured in seven different ways: band measurement, laser measurement with Vertex, global navigation satellite system, handheld mobile laser scanner, terrestrial laser scanner, drone, and smartphone with a light detection and ranging sensor. All the methods were compared in terms of accuracy, price, user-friendliness, and time required to obtain results. The calculated pile volume, depending on the method, varied from 2588 to 3362 m3. The most accurate results were provided by the terrestrial laser scanning method, which, however, was the most expensive and the most demanding in terms of collecting and evaluating the results. From a time and economic point of view, the most effective methods were UAVs and smartphones with LiDAR.

Are Modern Market-Available Multi-Rotor Drones Ready to Automatically Inspect Industrial Facilities?

Industrial inspection is a well-known application area for unmanned aerial vehicles (UAVs), but are modern market-available drones fully suitable for inspections of larger-scale industrial facilities? This review summarizes the pros and cons of aerial large-scale facility inspection, distinguishing it from other inspection scenarios implemented with drones. Moreover, based on paper analysis and additionally performed experimental studies, it reveals specific issues related to modern commercial drone software and demonstrates that market-available UAVs (including DJI and Autel Robotics) more or less suffer from the same problems. The discovered issues include a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) shift, an identification of multiple images captured from the same point, limitations of custom mission generation with external tools and mission length, an incorrect flight time prediction, an unpredictable time of reaching a waypoint with a small radius, deviation from the pre-planned route line between two waypoints, a high pitch angle during acceleration/deceleration, an automatic landing cancellation in a strong wind, and flight monitoring issues related to ground station software. Finally, on the basis of the paper review, we propose solutions to these issues, which helped us overcome them during the first autonomous inspection of a 2400 megawatts thermal power plant.

Performance Evaluation of Real-Time Kinematic Global Navigation Satellite System with Survey-Grade Receivers and Short Observation Times in Forested Areas

The Real-Time Kinematic (RTK) method is currently the most widely used method for positioning using Global Navigation Satellite Systems (GNSSs) due to its accuracy, efficiency and ease of use. In forestry, position is a critical factor for numerous applications, with GNSS currently being the preferred solution for obtaining such data. However, the decreased performance of GNSS observations in challenging environments, such as under the forest canopy, must be considered. This paper analyzes the performance of a survey-grade GNSS receiver under coniferous/deciduous tree cover. Unlike most previous research concerning this topic, the focus here is on employing a methodology that is as close as possible to real working conditions in the field of forestry. To achieve this, short observation times of 30 s were used, with corrections received directly in the field from a Continuously Operating Reference Station (CORS) of the national RTK network in Romania. In total, 84 test points were determined, randomly distributed under the canopy, with reference data collected by topographical surveys using total station equipment. In terms of the overall horizontal accuracy, an RMSE of 2.03 m and MAE of 1.63 m are found. Meanwhile, the overall vertical accuracy is lower, as expected, with an RMSE of 4.85 m and MAE of 4.01 m. The variation in GNSS performance under the different forest compositions was found to be statistically significant, while GNSS-specific factors such as DOP values only influenced the precision and not the accuracy of observations. We established that this methodology offers sufficient accuracy, which is application-dependent, even if the majority of GNSS solutions were code-based, rather than carrier-phase-based, due to strong interference from the vegetation.

Adaptive IMU error correction algorithm for dual-antenna GNSS/IMU integrated vehicle attitude determination

Abstract The attitude of a vehicle can be largely determined by integrating the global navigation satellite system (GNSS) and inertial measurement unit (IMU) in land-based applications. However, traditional dual-antenna GNSS/IMU integrated systems are susceptible to signal reflection, diffraction, and even interruption, leading to reduced accuracy and reliability in challenging environments. To address this issue, this paper proposes an adaptive IMU error correction algorithm for dual-antenna GNSS/IMU integrated vehicle attitude determination. First, we propose an IMU-aided baseline length constraint model to support the ambiguity resolution (AR) process. This model incorporates accurate prior information from the IMU, improving the success rate of AR within the dual-antenna GNSS/IMU integrated system. Second, we present an adaptive IMU error correction mechanism based on long short-term memory (LSTM) and particle swarm optimization (PSO) to assist in vehicle attitude determination and mitigate attitude error drift of the lMU during GNSS outages. Field test results verified that, compared to two other candidate algorithms, the proposed algorithm improves accuracy in roll, pitch, and yaw by 19.23%, 30.56%, and 67.12%, respectively, and by 12.50%, 10.71%, and 38.39% respectively. Moreover, in two distinct scenarios where GNSS is blocked for 120 seconds, the proposed algorithm still provides accurate and stable attitude information for vehicle navigation, with the accuracy of roll, pitch, and yaw kept within 0.08 degrees.

Autonomous Lunar Rover Localization while Fully Scanning a Bounded Obstacle-Rich Workspace.

This article addresses the scanning path plan strategy of a rover team composed of three rovers, such that the team explores unknown dark outer space environments. This research considers a dark outer space, where a rover needs to turn on its light and camera simultaneously to measure a limited space in front of the rover. The rover team is deployed from a symmetric base station, and the rover team's mission is to scan a bounded obstacle-rich workspace, such that there exists no remaining detection hole. In the team, only one rover, the hauler, can locate itself utilizing stereo cameras and Inertial Measurement Unit (IMU). Every other rover follows the hauler, while not locating itself. Since Global Navigation Satellite System (GNSS) is not available in outer space, the localization error of the hauler increases as time goes on. For rover's location estimate fix, one occasionally makes the rover home to the base station, whose shape and global position are known in advance. Once a rover is near the station, it uses its Lidar to measure the relative position of the base station. In this way, the rover fixes its localization error whenever it homes to the base station. In this research, one makes the rover team fully scan a bounded obstacle-rich workspace without detection holes, such that a rover's localization error is bounded by letting the rover home to the base station occasionally. To the best of our knowledge, this article is novel in addressing the scanning path plan strategy, so that a rover team fully scans a bounded obstacle-rich workspace without detection holes, while fixing the accumulated localization error occasionally. The efficacy of the proposed scanning and localization strategy is demonstrated utilizing MATLAB-based simulations.

Ionospheric Total Electron Content Response to the Intense Geomagnetic Storm of 10th -11th May 2024 over Low, Mid and High Latitude Regions

In this paper, we investigated the response of ionospheric Total electron content (TEC) to the intense geomagnetic storm of 10th - 11th May 2024 using 6 Global Navigation Satellite System (GNSS) stations: BAKE (Geog. Lat.64.33o N; Geog. Lon. 96.02o W), BELE (Geog. Lat. 1.41o S; Geog. Lon. 48.46o W), MBAR (Geog. Lat. 0.60o S; Geog. Lon. 30.74o E), SUTH (Geog. Lat. 32.38o S; Geog. Lon. 20.81o E), BJFS (Geog. Lat. 39.61o N; Geo. Lon. 115.89o E) and DUND (Geog. Lat. 45.88o S; Geog. Lon.170.60o E) situated over low, mid and high latitude regions. The GPS-TEC data was extracted, processed and used to plot vertical total electron content (VTEC) verses universal time (UT) from 8th to 13th May 2024 for each GNSS receiver station. A contour plot of TEC variation was also plotted for each station from 8th to 13th May 2024. The results showed TEC enhancing significantly at the beginning of the storm, during daytime at the geomagnetic equator, with the exception of MBAR, where TEC increased at night. This was attributed to the effect of prompt penetration of electric field (PPEF). A reduction in TEC was also noted on 11th May 2024, during the recovery phase over all the GNSS receiver stations. This was attributed to the effect of the disturbance dynamo electric field (DDEF) and composition change. The contour plots showed diurnal variation in TEC concentration over each GNSS receiver station. The TEC concentration however reduced during the storm period.

Inter-comparison of GNSS- IPWV with ERA-5 IPWV and monitoring of convective events over the Indian region

The present study deals with (i) An Inter-comparison study of ground based Global Navigation Satellite System (GNSS) derived Integrated Precipitable Water Vapour (IPWV) with 5th generation global climate reanalysis data of European Centre for Medium Range Weather Forecast (ERA-5) (ii) IPWV thresholds of GNSS data (iii) case studies of IPWV analysis. It is found that both the datasets (GNSS and ERA-5) are strongly correlated & the correlation coefficient ranging between 0.97 and 0.99. Monthly Thresholds of IPWV (MTI) are generated with 2017 -2020 data sets from Indian GNSS station having continuous data and found very useful input as value addition to know the possibility of building up /decaying of convection in day to day daily forecast issued to the public. Case studies shows an increase of IPWV, 3 to 4 hour prior to the occurrence of convective event around the GNSS station and found very useful for nowcasting. Therefore, utilization of IPWV (model as wells GNSS based) have incremented value in enriching the understanding about the weather event and its forecasting. This supplement information along with other analysis products (model, surface, upper air, satellite or radar etc) can support further in appropriate decision making to the forecasters, decision makers and the end users of the society.

Spoofing attack recognition for GNSS-based train positioning using a BO-LightGBM method.

Trustworthy positioning is critical in the operational control and management of trains. For a train positioning system (TPS) based on a global navigation satellite system (GNSS), a spoofing attack significantly threatens the trustworthiness of positioning. However, the influence and recognition of GNSS spoofing attacks are not considered in the existing research on GNSS-enabled TPS. Spoofing attacks affect the performance of GNSS observations and the positioning results, allowing the development of data-driven spoofing recognition solutions. This study aims to achieve effective spoofing recognition for active security protection in TPS. Different features were designed to reflect the effects of a spoofing attack, including GNSS observation-related indicators and odometer-enabled parameters, and a novel Bayesian optimization-light gradient boosting machine (BO-LightGBM) solution was proposed. In particular, a Bayesian optimization technique was introduced into the LightGBM framework to improve the hyperparameter determination capability for recognition model training. Using a GNSS spoofing test platform with a specific GNSS signal generator and the SimSAFE spoofing test tool, different spoofing attack modes were tested to collect sample datasets for model training and evaluation. The results of model establishment and comparison of the model performance indicators illustrated the advantages of the proposed solution, its adaptability to different spoofing attack situations, and its superiority over state-of-the-art modeling strategies.