Global Navigation Satellite System Signals Research Articles

UAV Geo-Localization Dataset and Method Based on Cross-View Matching

The stable flight of drones relies on Global Navigation Satellite Systems (GNSS). However, in complex environments, GNSS signals are prone to interference, leading to flight instability. Inspired by cross-view machine learning, this paper introduces the VDUAV dataset and designs the VRLM network architecture, opening new avenues for cross-view geolocation. First, to address the limitations of traditional datasets with limited scenarios, we propose the VDUAV dataset. By leveraging the virtual–real mapping of latitude and longitude coordinates, we establish a digital twin platform that incorporates 3D models of real-world environments. This platform facilitates the creation of the VDUAV dataset for cross-view drone localization, significantly reducing the cost of dataset production. Second, we introduce a new baseline model for cross-view matching, the Virtual Reality Localization Method (VRLM). The model uses FocalNet as its backbone and extracts multi-scale features from both drone and satellite images through two separate branches. These features are then fused using a Similarity Computation and Feature Fusion (SCFF) module. By applying a weighted fusion of multi-scale features, the model preserves critical distinguishing features in the images, leading to substantial improvements in both processing speed and localization accuracy. Experimental results demonstrate that the VRLM model outperforms FPI on the VDUAV dataset, achieving an accuracy increase to 83.35% on the MA@20 metric and a precision of 74.13% on the RDS metric.

Reduction of Multipath Effect in GNSS Positioning by Applying Pseudorange Acceleration as Weight

A novel approach is proposed to mitigate the multipath effect, considered a major source of error in global navigation satellite system (GNSS) positioning in urban areas. We utilize code pseudorange acceleration measurements as a weight in a least squares estimation process. If GNSS signals are reflected off a surrounding surface, they cause large variations in the recorded pseudorange measurement. Accelerations computed at each epoch with three consecutive pseudoranges exhibit significant fluctuations in a multipath signal. As a result, positioning accuracy improved by 75% horizontally and 79% vertically compared to not applying any weight. Even when multipath errors exist, the range acceleration (RA) value is sometimes low at many epochs. When a threshold value for the signal-to-noise ratio was additionally applied besides RA, the positioning accuracy at two test sites (including a deep urban environment) improved by more than 80% in both horizontal and vertical directions.

A systematic analysis of scan matching techniques for localization in dense orchards

In recent years, different methods have been studied to determine machinery position within a grove, as an alternative for complementing GNSS (global navigation satellite system) information in cases where GNSS signal is occluded. Such a situation can be observed when agricultural machinery travels under dense foliage or on the slopes of mountains. Scan matching techniques arise as a possible solution for localizing the machinery, complementing the absence of the GNSS signal when necessary. However, since key points are difficult to obtain in heterogeneous, unstructured and non-rigid environments (such as orchard plants), the performance of scan matching techniques often decreases in agricultural environments. This paper suggests dividing the point clouds into horizontal and vertical segments to improve the performance of scan-matching methods in orchards. It also examines the best way for registered frames to overlap. We validate the analysis with extensive experimentation in a Fuji apple orchard. The results show that the cumulative localization error in scan matching techniques can be notoriously decreased with selective parts of the orchard, by up to 60%. The experimentation performed herein suggests that the proposed methodology can complement the GNSS navigation in a middle-long path.

The Accuracy of RTK enabled Mobile Phones in RTK denied Areas

Abstract. Surveyors often encounter challenges when tasked with measuring points in regions where buildings disrupt satellite visibility. The precise surveying of points in these areas typically relies on employing total stations. This method effectively extends high precision from unaffected zones (where satellite visibility is assured) to those areas where disruption occurs due to buildings. Total stations achieve this by executing distance and angle measurements from a reference point coordinate to the location where satellite visibility is compromised. The process is notably time-consuming, thereby incurring significant costs. Moreover, the intricate nature of this surveying process necessitates specialized skills and expertise, contributing further to the overall expense. In this study, we investigate an approach based on RTK (Real-Time Kinematic) enabled mobile phones and photogrammetry to achieve the same. In principle, this approach has a similar philosophy. However, instead of working with a few points with good satellite visibility that are obtained by a GNSS (Global Navigation Satellite System) rover, the procedure works with hundreds of observations with varying accuracy. Those accuracies depend on the satellite visibility. Each observation captures an image from the mobile phone together with the GNSS signal and its accuracy. Angle and distance measurements that traditionally are observed by a total station are retrieved by photogrammetry on the total image collection.

An Efficient Attentive-GRU Structure for Uncertainty Modelling of Crowdsourced Human Trajectories Under Building-obscured Urban Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of human mobility analysis and urban navigation, that can affect the performance of human behaviour modelling and location information acquisition. Existing uncertainty modelling algorithms towards the human movement trajectory are subjected to random and highly dynamic human motion characteristics and sampling and observation errors of Global Navigation Satellite System (GNSS) signals caused by the occlusion of buildings in urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of uncertainty modelling. To fill in this gap, this paper proposes an efficient attentive-GRU structure for uncertainty modelling of crowdsourced human trajectories under building-obscured urban scenes, that takes into account both temporal correlation and spatial correlation of human-originated GNSS trajectories and related motion features. A period of human motion data is modelled instead of only one or adjacent location points to avoid interference factors caused by the obstruction of urban buildings, and time-varying measurement and sampling errors are further estimated and combined with comprehensive human motion features to improve the accuracy of final uncertainty modelling. Comprehensive experiments indicate that compared with existing uncertainty modelling methods including physical models and deep-learning models, the proposed attentive-GRU structure realizes much better performance under different accuracy indexes.

Global Navigation Satellite System Signal Phase Combining and Performance of Distributed Antenna Arrays

Global Navigation Satellite System Signal Phase Combining and Performance of Distributed Antenna Arrays

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.

A Novel FECAM-iTransformer Algorithm for Assisting INS/GNSS Navigation System during GNSS Outages

In the field of navigation and positioning, the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system is known for providing stable and high-precision navigation services for vehicles. However, in extreme scenarios where GNSS navigation data are completely interrupted, the positioning accuracy of these integrated systems declines sharply. While there has been considerable research into using neural networks to replace the GNSS signal output during such interruptions, these approaches often lack targeted modeling of sensor information, resulting in poor navigation stability. In this study, we propose an integrated navigation system assisted by a novel neural network: an inverted-Transformer (iTransformer) and the application of a frequency-enhanced channel attention mechanism (FECAM) to enhance its performance, called an INS/FECAM-iTransformer integrated navigation system. The key advantage of this system lies in its ability to simultaneously extract features from both the time and frequency domains and capture the variable correlations among multi-channel measurements, thereby enhancing the modeling capabilities for sensor data. In the experimental part, a public dataset and a private dataset are used for testing. The best experimental results show that compared to a pure INS inertial navigation system, the position error of the INS/FECAM-iTransformer integrated navigation system reduces by up to 99.9%. Compared to the INS/LSTM (long short-term memory) and INS/GRU (gated recurrent unit) integrated navigation systems, the position error of the proposed method decreases by up to 82.4% and 78.2%, respectively. The proposed approach offers significantly higher navigation accuracy and stability.

A Cumulant-Based Method for Acquiring GNSS Signals.

Global Navigation Satellite Systems (GNSS) provide positioning, velocity, and time services for civilian applications. A critical step in the positioning process is the acquisition of visible satellites in the sky. Modern GNSS systems, such as Galileo-developed and maintained by the European Union-utilize a new modulation technique known as Binary Offset Carrier (BOC). However, BOC signals introduce multiple side-peaks in their autocorrelation function, which can lead to significant errors during the acquisition process. In this paper, we propose a novel acquisition method based on higher-order cumulants that effectively eliminates these side-peaks. This method is capable of simultaneously acquiring both conventional ranging signals, such as GPS C/A code, and BOC-modulated signals. The effectiveness of the proposed method is demonstrated through the acquisition of simulated signals, with a comparison to traditional methods. Additionally, we apply the proposed method to real satellite signals to further validate its performance. Our results show that the proposed method successfully suppresses side-peaks, improves acquisition accuracy in weak signal environments, and demonstrates potential for indoor GNSS applications. The study concludes that while the method may increase computational load, its performance in challenging conditions makes it a promising approach for future GNSS receiver designs.

A new approach for GNSS spoofing detection using power and signal quality monitoring

Abstract Global Navigation Satellite System (GNSS) signals are highly susceptible to spoofing attacks. Signal Quality Monitoring (SQM) methods are simple and easy to detect spoofing. However, traditional SQM methods are only effective for matched-power cases and exhibit high detection probability only for a short time. This study introduces a new approach, exploiting anomalies in receiver correlation outputs during spoofing. It efficiently detects signal amplitude fluctuations and correlation peak distortions. The Texas Spoofing Test Battery (TEXBAT) dataset is used to evaluate the detection performance of the proposed approach. The results show that the proposed approach has excellent detection performance in various spoofing cases, including matched-power, overpowered, static, and dynamic cases. This approach surpasses traditional SQM metrics in detection probability and sensitivity to spoofing stages. Importantly, the proposed approach detects spoofing early.

A Method for Assisting GNSS/INS Integrated Navigation System during GNSS Outage Based on CNN-GRU and Factor Graph

In complex urban road environments, vehicles inevitably experience frequent or sustained interruptions of the Global Navigation Satellite System (GNSS) signal when passing through overpasses, near tall buildings, and through tunnels. This results in the reduced accuracy and robustness of the GNSS/Inertial Navigation System (INS) integrated navigation systems. To improve the performance of GNSS and INS integrated navigation systems in complex environments, particularly during GNSS outages, we propose a convolutional neural network–gated recurrent unit (CNN-GRU)-assisted factor graph hybrid navigation method. This method effectively combines the spatial feature extraction capability of CNN, the temporal dynamic processing capability of GRU, and the data fusion strength of a factor graph, thereby better addressing the impact of GNSS outages on GNSS/INS integrated navigation. When GNSS signals are strong, the factor graph algorithm integrates GNSS/INS navigation information and trains the CNN-GRU assisted prediction model using INS velocity, acceleration, angular velocity, and GNSS position increment data. During GNSS outages, the trained CNN-GRU assisted prediction model forecasts pseudo GNSS observations, which are then integrated with INS calculations to achieve integrated navigation. To validate the performance and effectiveness of the proposed method, we conducted real road tests in environments with frequent and sustained GNSS interruptions. Experimental results demonstrate that the proposed method provides higher accuracy and continuous navigation outcomes in environments with frequent and sustained GNSS interruptions, compared to traditional GNSS/INS factor graph integrated navigation methods and long short-term memory (LSTM)-assisted GNSS/INS factor graph navigation methods.

Chip-scale atomic clock (CSAC) aided GNSS in urban canyons

In urban canyons, the reflections and obstructions of Global Navigation Satellite System (GNSS) signals frequently lead to significant errors in measurements, the number of which can be larger than that of the correct measurements. This leads to a severe degradation of GNSS performance in urban canyons. Various fault detection and exclusion (FDE) algorithms have been developed to cope with these outliers caused by multipath effects. Most of these FDE algorithms check the consistency among measurements. However, in urban canyons, their effectiveness is significantly compromised by the lack of fault-free measurements. There is an urgent need to develop new constraints for enhancing GNSS FDE performance. In recent years, the advent of Chip-Scale Atomic Clock (CSAC), known for their affordability and high frequency stability, offers a promising solution for accurately predicting receiver clock errors. Additionally, using city maps to establish height constraints is another way to increase redundancy. The purpose of this study is to improve the GNSS positioning accuracy in urban canyons with the aid of CSAC and city map data. A novel FDE algorithm is developed to search for positions through the constraints of height and receiver clock. Extensive tests were conducted in urban canyons to evaluate the performance of the system. Results showed that the positioning accuracy can be improved from tens of meters to less than 6 m.

Adaptive phase lock loop system for global navigation satellite systems signals

Formulation of the problem. Classical optimal Bayesian phase lock loop (PLL) algorithms require a priori knowledge of the phase dynamics parameters and signal-to-noiz ratios of the received signals. In practice, these parameters vary significantly and, as a rule, are unknown. Taking into account the fact that the operation of such algorithms under conditions different from those a priori specified is not reliable according to the criterion of minimum variance error. Purpose of the study. Develop a phase-locked loop system for the Global Navigation Satellite System (GNSS) signal that adapts to phase dynamics and signal-to-noise ratio to maintain phase tracking over the widest possible range of operating conditions. Results. A multi-channel adaptive phase-locked loop system (MAPLL system) has been developed. Practical significance. The developed system is capable of processing a jump in the signal-to-noise ratio from 50 to 9 dBHz and back without losing phase tracking (under conditions of low dynamics), maintaining phase tracking during abrupt transitions of dynamics between low (due only to the dynamics of the reference oscillator) and high (sinusoidal acceleration 10g and sinusoidal jerk 10 g/s) at a signal-to-noise ratio of 24 dBHz. Thus, in real-world conditions where object dynamics and S/N ratios of received signals change in unpredictable ways, MAPLL system maintains phase tracking over a much wider range of conditions than non-adaptive PLL.

Statistical analysis and effects of radio frequency interference in GPS signal quality in Thailand

The radio frequency interference (RFI) in global navigation satellite system (GNSS) signals has recently received much attention in the GNSS community because of frequent jamming issues. The carrier-to-noise density ratio (C/N0) is one of the common parameters to indicate the signal quality. In this work, we propose a real-time RFI analysis based on windowing and normalization of C/N0 observations. Specifically, the percentage of RFI values are analyzed based on the modified RFI detection. The steps to analyze the RFI levels (low, medium, high) are highlighted. In addition, we analyzed the occurrences of local RFI effects in areas surrounding the Suvarnabhumi International Airport as well as remote areas. We validate the modified RFI detection by using the GNSS reference stations at the urban, suburban, and outside the capital city in Thailand. The user positioning errors with the high (severe) RFI levels are investigated based on the single point positioning (SPP) and real-time kinematics (RTK). From the experimental simulations, the high RFI levels at the urban are higher than those at the suburban. As expected, the statistical analysis covering COVID-19 (2019 to 2023) shows that the high RFI levels in June 2023 (post COVID-19) are more than those in June 2020 and 2021 (lockdown COVID-19) by about twofold. Additionally, the SPP positioning errors with the medium/high RFI levels are clearly seen. There are more floating solutions in the RTK system in the year with more RFI presence.

Wideband circularly polarized high accuracy survey active antenna for global navigation satellite systems applications

AbstractThis letter presents a new circularly polarized (CP) active antenna for global navigation satellite systems (GNSS) operating in the L1‐band (1.164–1.28 GHz) and L2‐band (1.525–1.615 GHz). The active antenna consists of a passive antenna and a two‐channel low‐noise amplifier (LNA) circuit. To achieve wideband coverage of the L1/L2 bands while maintaining a low profile, a new electroplating process is utilized in fabricating the passive antenna. Additionally, to realize CP performance, the passive antenna is sequentially fed by four ports with a wideband feeding network. To minimize phase variations, a 90° phase shifter loaded with open‐short‐stub is integrated into the feeding network. Furthermore, to improve noise immunity, out‐of‐band rejection, and amplify GNSS signals, the LNA circuit cascades two‐stage dielectric bandpass filters and three‐stage LNAs. Consequently, the circuit features a low noise figure of below 1.65 dB, a maximum out‐of‐band rejection level of 60 dB, and a stable active gain of 30 dB. The performance of the active antenna is evaluated in an outdoor open field using a GNSS receiver.

Preliminary insights on fast GNSS signal capture using SFT and FFT frequency shift

Global Navigation Satellite Systems (GNSS) are the cornerstone of modern navigation and positioning technology. In this paper, we introduce an innovative approach to GNSS signal acquisition, specifically designed to enhance the performance and speed of the signal lock-on process with real-world applications in mind. Our proposed algorithm leverages advanced mathematical tools, including Fast Fourier Transform (FFT) and Inverse FFT, to streamline the acquisition process. Notably, it eliminates the need for an Intermediate Frequency (IF) down mixer, simplifying hardware and potentially reducing power consumption in GNSS receivers. One key breakthrough is using the FFT's "circle shift" to handle Doppler frequency offsets resulting from relative satellite-receiver motion. This method significantly reduces the computational burden during Doppler frequency search, ensuring a faster and more efficient lock-on to satellite signals. Furthermore, we exploit the sparse nature of GNSS signals in the reverse FFT calculation, utilizing a Sparse Fourier Transform (SFT) for efficient processing. This innovation allows quicker reverse FFT computations, pivotal in the acquisition process. Through extensive simulations, we demonstrate the superior performance of our improved algorithm. Its speed and reliability make it well-suited for many practical GNSS applications, such as vehicle navigation, precision agriculture, surveying, and geolocation services. By enhancing acquisition efficiency, our approach contributes to more accurate, responsive, and energy-efficient GNSS positioning, benefiting users in both civilian and industrial sectors.

A Hybrid Algorithm of LSTM and Factor Graph for Improving Combined GNSS/INS Positioning Accuracy during GNSS Interruptions.

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss.

An experimental analysis of outdoor UAV localisation through diverse estimators and crowd-sensed data fusion

Motivated by the challenge of achieving precise 3D outdoor localisation for unmanned aerial vehicles (UAVs) in global navigation satellite system (GNSS)-denied environments, this paper introduces an innovative technique. Integrating crowd-sensed data fusion to counter inertial navigation system (INS) drift during GNSS signal outages, the proposed method exploits diverse estimators to enhance its efficacy. A micro lightweight frequency modulated continuous wave (FMCW) radar mounted on the UAV captures ground scatterer reflections, processed via fast Fourier transform (FFT) to generate a range-Doppler map. This map facilitates forward velocity estimation during GNSS signal loss. This approach employs adaptive thresholding, image binarisation, and connected components-based techniques for target detection from a computer vision standpoint. The derived radar-based velocity fuses with magnetometer, barometer, and inertial measurement unit (IMU) data using diverse estimators like extended Kalman filter (EKF) and particle filter (PF). Real-time flight data evaluation and simulated outage periods using EKF and PF validate the outdoor localisation system. Experimental analyses demonstrate substantial improvements, enhancing 3D positioning accuracy by 99.89% and 99.83% for the initial and subsequent flights, respectively, leveraging PF to fortify the standalone INS mode during GNSS signal loss. This approach significantly enhances UAV localisation precision, particularly in challenging GNSS-denied scenarios, showcasing the potential for real-world applications.

Investigating Different Interpolation Methods for High-Accuracy VTEC Analysis in Ionospheric Research

The dynamic structure of the ionosphere and its changes play an important role in comprehending the natural cycle by linking earth sciences and space sciences. Ionosphere research includes a variety of fields like meteorology, radio wave reflection from the atmosphere, atmospheric anomaly detection, the impact on GNSS (Global Navigation Satellite Systems) signals, the exploration of earthquake precursors, and the formation of the northern lights. To gain further insight into this layer and to monitor variations in the total electron content (TEC), ionospheric maps are created using a variety of data sources, including satellite sensors, GNSS data, and ionosonde data. In these maps, data deficiencies are addressed by using interpolation methods. The objective of this study was to obtain high-accuracy VTEC (Vertical Total Electron Content) information to analyze TEC anomalies as precursors to earthquakes. We propose an innovative approach: employing alternative mathematical surfaces for VTEC calculations, leading to enhanced change analytical interpretation for anomaly detections. Within the scope of the application, the second-degree polynomial method, kriging (point and block model), the radial basis multiquadric, and the thin plate spline (TPS) methods were implemented as interpolation methods. During a 49-day period, the TEC values were computed at three different IGS stations, generating 1176 hourly grids for each interpolation model. As reference data, the ionospheric maps produced by the CODE (Center for Orbit Determination in Europe) Analysis Center were used. This study’s findings showed that, based on statistical values, the TPS model offered more accurate results than other methods. Additionally, it has been observed that the peak values in TEC calculations based on polynomial surfaces are eliminated in TPSs.

Bistatic radar cross section estimation by using DDM for better spatial resolution of ocean surface wind speed

The delay-Doppler map (DDM) is the signal power distribution of the Coarse/Acquisition code (C/A code) of the received Global Navigation Satellite System (GNSS) signal in different code phase delay and Doppler frequency. When the received signal is reflected from ocean surface, the DDM can be used to retrieve the ocean surface wind speed, which is the GNSS-reflectometry (GNSS-R) technique. Due to the signal power distribution in DDM is the correlation results of received and artificial C/A code from receiver in different code phase delay and Doppler frequency, the Woodward ambiguity function (WAF) occurs in the DDM. In the case of DDM, the WAF is the correlation results of two square waves in different code phase delay and Doppler frequency, and is approximated a triangular function and a sinc function in code phase delay and Doppler frequency axes, respectively. It means the correlation results not only show the code phase delay and Doppler frequency of the received signal but also influence the surrounding code phase delay and Doppler frequency values and cause the structure of DDM more complex. Using more bins in DDM in the wind speed retrieval process can reduce the influence of WAF but cause the spatial resolution to worsen. In order to use as less bins as possible in the retrieval process and not reduce the retrieval efficiency too much, a simple method to estimate bistatic radar cross section (BRCS) from DDM is developed in this study. Furthermore, a retrieval process is also developed for ocean surface wind speed retrieving by using less bins in the DDM from Cyclone Global Navigation Satellite System (CYGNSS) mission.