GNSS Positioning Research Articles

Multifrequency Signal Characterization and Assessment of GNSS Positioning in Smartphone

Multifrequency Signal Characterization and Assessment of GNSS Positioning in Smartphone

Precise Point Positioning and Differential Solutions by Online GNSS Calculation Tools and RTKLIB: A Comparative Study

The recent advances in GNSS positioning of the recent decades have been possible by the development of increasingly efficient software and online calculation tools. The differences between these online PPP calculation tools result in a different level of performance. Our study shows that for 24-hour or 6-hour observation time, the Canadian Spatial Reference System for PPP (CSRS-PPP), CenterPoint RTX Post-Processing (RTX), Magic/GNSS, Institut Geographique National-PPP (IGN-PPP) and RTKLIB tools have almost similar level of performance with International Terrestrial Reference Frame (ITRF) solutions considered as reference solution. Average deviations on the three components X, Y and Z for the different tools compared to ITRF solutions do not exceed 1 cm. However, the CSRS-PPP tool gives deviations of less than 5 mm. Calculations from the observations of 2 h and 1 h show that the RTX and CSRS-PPP tools keep deviations similar to those obtained with 24 h and 6 h, while RTKLIB and IGN-PPP give deviations exceeding 6 cm and sometimes failures of some calculations for IGN-PPP.

Extended Principal Component Analysis for Spatiotemporal Filtering of Incomplete Heterogeneous GNSS Position Time Series

When ordinary principal component analysis (PCA) is employed to analyze the position time series of a regional GNSS station network, the GNSS time series are assumed to be homogeneous, and the missing data in the time series must be restored beforehand. To directly process incomplete and heterogeneous GNSS position time series, we develop the extended PCA (EPCA) and weighted EPCA approaches to solving for the missing values based on the best low-rank approximation in the spatiotemporal domain. The proposed approaches are used to process the real GNSS position time series of 24 stations in North China spanning 2011 to 2019 and successfully extract the common mode errors (CMEs). The proposed approaches are compared with modified PCA (MPCA) and weighted MPCA, in which an additional optimization criterion needs to be introduced in the frequency domain. The results show that EPCA can extract more CMEs than MPCA for both the unweighted and weighted cases. Consequently, EPCA outperforms MPCA in reducing noise and improving the accuracy of site velocity estimates. Repeated simulation experiments show that the CMEs extracted by EPCA are closer to the simulated true values than those extracted by MPCA. When the formal errors of the time series are considered, both weighted EPCA and weighted MPCA outperform their unweighted counterparts, and the former outperforms the latter. In addition, EPCA is computationally more efficient than MPCA since fewer unknowns need to be estimated.

Performance Evaluation of GNSS Position Augmentation Methods for Autonomous Vehicles in Urban Environments.

Global Navigation Satellite Systems provide autonomous vehicles with precise position information through the process of position augmentation. This paper presents a series of performance tests aimed to compare the position accuracy of augmentation techniques such as classical Differential Global Navigation Satellite System, Real-time Kinematic and Real-time eXtended. The aim is to understand the limitations and choose the best position augmentation technique in order to obtain accurate, trustworthy position estimates of a vehicle in urban environments. The tests are performed in and around the German cities of Wuppertal and Duesseldorf, using a vehicle fitted with the navigation system POS-LV 220, developed by Applanix Corporation. In order to evaluate the real-time performance of position augmentation techniques in a highly challenging environment, a total of four test regions are selected. The four test regions are characterized mainly by uneven terrain with tall buildings around the University of Wuppertal, flat terrain with roads of varying width in the city centre of Wuppertal and Duesseldorf and flat terrain in a tunnel section located in the city of Wuppertal. The performances of the different position augmentation are compared using a Root Mean Square (RMS) error estimate obtained as an output from the Applanix system. Furthermore, a High-Definition map of the environment is used for the purpose of model validation, which justifies the use of RMS error estimate as an evaluation metric for the performance analysis tests. According to the performance tests carried out as per the conditions specified in this paper, the Real-time eXtended (RTX) position augmentation method enables to obtain a more robust position information of the vehicle than Real-time Kinematic (RTK) method, with a typical accuracy of a few centimeter in an urban environment.

MT-e&R: NMEA Protocol-Assisted High-Accuracy Navigation Algorithm Based on GNSS Error Estimation Using Multitask Learning

Accurate location data of ground vehicles is very important for various intelligent transportation applications. As one of the most commonly-used navigation solutions at present, the GNSS/INS integrated navigation still cannot meet the accuracy and stability command of current applications. This paper presents a National Marine Electronics Association (NMEA) protocol data-assisted high-accuracy navigation algorithm based on GNSS position error estimation using multi-task learning (MT-e&R), which can accurately estimate the GNSS position error and GNSS measurement noise covariance matrix with the assistance of protocol data and a multi-task learning model. Extensive experimental results on practical navigation data collected in various urban environments of Beijing demonstrate that our proposed approach can improve the performance of integrated navigation system. The positioning errors of integrated navigation equipped with single-frequency receiver are reduced by 36.17% and 39.58% for double-frequency receiver, which confirms the reasonable environmental adaptability of our proposed MT-e&R algorithm.

Ionospheric scintillation characteristics and their effects over GNSS signal and position and navigation systems in the Brazilian region

The ionosphere is an ionized layer extending from about 50 km to 1,000 km of altitude. When an electromagnetic signal cross this layer it suffers a delay in its group velocity and an advance in its phase velocity. The ionosphere is very dynamics and after the sunset its F region equatorial bottomside is lifted up by the intensified eastward electric field, giving origin to an steep plasma gradient. This configurates an unstable condition with higher density plasma standing over lower density one. This instability pushes rarified plasma upward giving origin to large regions named Equatorial Plasma Bubbles (EPB) that rise at equatorial regions and map to low latitudes along the magnetic field lines and can reach continental extension. Through cascating process small scale irregularities (cm to km) are generated inside the EPBs. Due to refractive effects amplitude and phase scintillations are generated in the signal crossing these irregularities. Trans-ionospheric signals used in telecommunication links and in the GNSS applications are severely affected during ionospheric scintillations. In this work we will present the ionospheric scintillation morphology over Brazilian longitudinal sector, its effects over positioning and navigation systems and the existing methodology to mitigate them.

An Aerial and Ground Multi-Agent Cooperative Location Framework in GNSS-Challenged Environments

In order to realize the cooperative localization of multi-unmanned platforms in the GNSS-denied environment, this paper proposes a collaborative SLAM (simultaneous localization and mapping, SLAM) framework based on image feature point matching. Without GNSS, a single unmanned platform UGV and UAV (unmanned ground vehicle, UGV; unmanned aerial vehicle, UAV) equipped with vision and IMU (inertial measurement unit, IMU) sensors can exchange information through data communication to jointly build a three-dimensional visual point map, and determine the relative position of each other through visual-based position re- identification and PnP (Perspective-n-Points, PnP) methods. When any agent can receive reliable GNSS signals, GNSS positioning information will greatly improve the positioning accuracy without changing the positioning algorithm framework. In order to achieve this function, we designed a set of two-stage position estimation algorithms. In the first stage, we used the modified ORB-SLAM3 algorithm for position estimation by fusing visual and IMU information. In the second stage, we integrated GNSS positioning and cooperative positioning information using the factor graph optimization (FGO) algorithm. Our framework consists of an UGV as the central server node and three UAVs carried by the UGV, that will collaborate on space exploration missions. Finally, we simulated the influence of different visibility and lighting conditions on the framework function on the virtual simulation experiment platform built based on ROS (robot operating system, ROS) and Unity3D. The accuracy of the cooperative localization algorithm and the single platform localization algorithm was evaluated. In the two cases of GNSS-denied and GNSS-challenged, the error of co-location reduced by 15.5% and 19.7%, respectively, compared with single-platform independent positioning.

An optimized stochastic model for smartphone GNSS positioning

With the increasing popularity of high-precision applications of smartphone, more and more scholars carry out studies in the field of smartphone GNSS positioning. In the previous studies, more attention has been paid to data quality control, data preprocessing and observation models. However, the research on stochastic models is rare. The stochastic model is significant for the subsequent optimal positioning parameter estimation, meanwhile, the stochastic models of smartphones and professional geodetic receivers are very different for the completely different characteristics of smartphone GNSS observations. It is necessary to develop a stochastic model suitable for smartphone observations. Based on the characteristics of smartphone observations, this paper proposes an optimized stochastic model. The specific process of this method is as follows: firstly, the code-minus-phase (C-L) combi-nation and double-differenced measurement were used to quantify the noise of smartphone code and phase observations. Then, an optimized carrier-to-noise density ratio (C/N0) dependent stochastic model was proposed on the basis of the characteristic of smartphone observations. To validate the superiority of the proposed model, single point positioning (SPP) and real time kinematic (RTK) experiments were carried out by Xiaomi 8 in 2 days. The 3-dimensional root mean squares (RMS) of SPP were 6.18 and 5.38 m, with improvements varying within 0.00%-10.02% compared with the customary models. Likewise, the RMS of RTK were 0.14 and 0.24 m, with improvements fitting in range of 10.06%-39.92%. This research plays an important role in improving the positioning accuracy of smartphone and promoting the popularization of high-precision applications.

Hassas Nokta Konumlama Yönteminde GNSS Ölçü Süresi-Konum Doğruluğu İlişkisinin Araştırılması

In this research, the effect of Precise Point Positioning (PPP) method on the position accuracy of GNSS points has been investigated and 8 TUSAGA-Active points were used. In an effort to improve the GNSS position accuracy, 24-hour RINEX data was evaluated using the GAMIT/GLOBK that academic software. The 24-hour datas for each stations divided into subgroups of data with TEQC software. These subgroups of data are 2-4-6-8 and 12 hours. Then, all subgroups of data were evaluated with online-based CSRS-PPP software. By accepting the 24-hour data of GAMIT/GLOBK software as correct, the mean squared errors were calculated from the results of the data sets obtained from CSRS-PPP. Furthermore, the relationships between measurement time/positioning accuracy were determined. These results were compared with Eckl et al. 2001. The position accuracies obtained from the PPP solutions were found to be compatible with the position accuracy relations obtained in Eckl et al. (2001).

Wide-Area GNSS Corrections for Precise Positioning and Navigation in Agriculture

This paper characterizes, with static and roving GNSS receivers in the context of precision agriculture research, the hybrid ionospheric-geodetic GNSS model Wide-Area Real-Time Kinematics (WARTK), which computes and broadcasts real-time corrections for high-precision GNSS positioning and navigation within sparse GNSS receiver networks. This research is motivated by the potential benefits of the low-cost precise WARTK technique on mass-market applications such as precision agriculture. The results from two experiments summarized in this work, the second one involving a working spraying tractor, show, firstly, that the corrections from the model are in good agreement with the corrections provided by IGS (International GNSS Services) analysis centers computed in post-processing from global GNSS data. Moreover, secondly and most importantly, we have shown that WARTK provides navigation solutions at decimeter-level accuracy, and the ionospheric corrections significantly reduce the computational time for ambiguity estimation: up to convergence times for the 50%, 75% and 95% of cases equal or below 30 s (single-epoch), 150 s and 600 s approximately, vs. 1000 s, 2750 s and 4850 s without ionospheric corrections, everything for a roving receiver at more than 100 km far away from the nearest permanent receiver. The real-time horizontal position errors reach up to 3 cm, 5 cm and 12 cm for 50%, 75% and 95% of cases, respectively, by constraining and continuously updating the ambiguities without updating the permanent receiver coordinates, vs. the 6 cm, 12 cm and 32 cm, respectively, in the same conditions but without WARTK ionospheric corrections.

Monte Carlo MSSA to modeling GNSS position time series and its implication for crustal deformation monitoring

In this paper, a scheme of signal extraction and modeling for GNSS position time series based on Monte Carlo Multi-channel Singular Spectrum Analysis (MC-MSSA) is introduced, which can effectively consider the spatial correlation of different directions by processing the different components of position time series at the same time. Meanwhile, the Monte Carlo significance test is utilized to distinguish the signal from the colored noise. By comparing with Singular Spectrum Analysis (SSA), it can be confirmed that MSSA has better signal extraction and modeling performance by taking into account the correlation of different channels. Then, taking the LHAZ station as an example, MC-MSSA is utilized to simultaneously model the three components of GNSS position time series, and the trend and periodic signals are respectively identified by Kendall nonparametric test and W-correlation correlation analysis. The result denotes that MC-MSSA can effectively model the tectonic and non-tectonic signals of GNSS position time series, and the modeled signals can more intuitively reflect the dynamic movement of the station. Finally, based on the MC-MSSA-modeled tectonic signal, we characterize the crustal deformations around the eastern Tibetan Plateau, mainly including the crustal movement and strain rate change. The results suggest that the pushing movement of the Tibetan Plateau from the Indian plate is blocked by the South China block, and the crustal movement rate is obviously decreased and appears a right-handed movement trend. Meanwhile, the junction of the Tibetan Plateau and South China block has accumulated a certain amount of stress, and the tectonic activity at the junction is relatively strong and still belongs to the dangerous zone of seismic activity.

Ionospheric Behavior during the 10 June 2021 Annular Solar Eclipse and Its Impact on GNSS Precise Point Positioning

The main effects of the 10 June 2021 annular solar eclipse on GNSS position estimation accuracy are presented. The analysis is based on TEC measurements made by 2337 GNSS stations around the world. TEC perturbations were obtained by comparing results 2 days prior to and after the day of the event. For the analysis, global TEC maps were created using ordinary Kriging interpolation. From TEC changes, the apparent position variation was obtained using the post-processing kinematic precise point positioning with ambiguity resolution (PPP-AR) mode. We validated the TEC measurements by contrasting them with data from the Swarm-A satellite and four digiosondes in Central/South America. The TEC maps show a noticeable TEC depletion (<−60%) under the moon’s shadow. Important variations of TEC were also observed in both crests of the Equatorial Ionization Anomaly (EIA) region over the Caribbean and South America. The effects on GNSS precision were perceived not only close to the area of the eclipse but also as far as the west coast of South America (Chile) and North America (California). The number of stations with positioning errors of over 10 cm almost doubled during the event in these regions. The effects were sustained longer (∼10 h) than usually assumed.

Regional Ionospheric Corrections for High Accuracy GNSS Positioning

Centimetre-level accurate ionospheric corrections are required for a high accuracy and rapid convergence of Precise Point Positioning (PPP) GNSS positioning solutions. This research aims to evaluate the accuracy of a local/regional ionospheric delay model using a linear interpolation method across Australia. The accuracy of the ionospheric corrections is assessed as a function of both different latitudinal regions and the number and spatial density of GNSS Continuously Operating Reference Stations (CORSs). Our research shows that, for a local region of 5° latitude ×10° longitude in mid-latitude regions of Australia (~30° to 40°S) with approximately 15 CORS stations, ionospheric corrections with an accuracy of 5 cm can be obtained. In Victoria and New South Wales, where dense CORS networks exist (nominal spacing of ~100 km), the average ionospheric corrections accuracy can reach 2 cm. For sparse networks (nominal spacing of >200 km) at lower latitudes, the average accuracy of the ionospheric corrections is within the range of 8 to 15 cm; significant variations in the ionospheric errors of some specific satellite observations during certain periods were also found. In some regions such as Central Australia, where there are a limited number of CORSs, this model was impossible to use. On average, centimetre-level accurate ionospheric corrections can be achieved if there are sufficiently dense (i.e., nominal spacing of approximately 200 km) GNSS CORS networks in the region of interest. Based on the current availability of GNSS stations across Australia, we propose a set of 15 regions of different ionospheric delay accuracies with extents of 5° latitude ×10° longitude covering continental Australia.

Non-Least Square GNSS Positioning Algorithm for Densely Urbanized Areas

The paper introduces an essentially new algorithm for calculating the GNSS position as an alternative to the least-square method. The proposed approach can be widely applied to any positioning method that uses multiple position lines for position calculation and is an example of how using a numerical solution can improve position accuracy without access to historical data. In essence, the method is based on the adaptation of the median filtering method widely used in the field of image processing, while at the same time applying a combinatorial approach and order statistics. The proposed solution makes it possible to improve on and assess the credibility of a single measurement. The article highlights the differences between the proposed and currently used approaches, as well as their advantages and disadvantages. The algorithm has been extensively tested under various environmental and weather conditions. The tests were carried out in typical and also in very demanding conditions, thus taking into account the real application context, i.e., pedestrian and car navigation in densely urbanized areas.

DESIGN AND EVALUATION OF GNSS/INS TIGHTLY-COUPLED NAVIGATION SOFTWARE FOR LAND VEHICLES

Abstract. Due to the development of society, the city center is full of high-rise buildings and the traffic becomes increasingly convenient; accordingly, there is a high demand for high-precision savigation in such areas. This paper studied the GNSS/Inertial Navigation System (INS) integrated navigation algorithm and developed software that can process GNSS data and IMU data. To verify the positioning performance of the algorithm, we collect the data of onboard GNSS and IMU in an urban environment and compare the results of GNSS positioning and GNSS/INS tightly-coupled navigation. The horizontal and 3D positioning accuracy of GNSS/INS tightly coupling is better than 0.8 m and 1.2 m, respectively. Compared to GNSS, the 3D and horizontal position precision of GNSS/INS tightly coupling improved by 17.4 % and 54.9 %, respectively. It proves that the GNSS/INS tightly-coupled navigation can provide higher-precision and more robust positioning results.

CONVOLUTIONAL NEURAL NETWORKS BASED GNSS SIGNAL CLASSIFICATION USING CORRELATOR-LEVEL MEASUREMENTS

Abstract. In urban areas, the None-Line-Of-Sight (NLOS) and Multipath (MP) signals are the major issues degrading the GNSS position accuracy. Signal reception type should be identified before correcting the NLOS or MP induced errors. Signal features, i.e., signal strength, change rate of received signal strength, difference between delta pseudo-range and pseudo-range rate, have been explored in signal reception type classification. In this letter, with the aim to improve the signal classification accuracy, we propose a new GNSS NLOS/MP/LOS signals classification method using the correlator-level measurements. Firstly, vector tracking (VT) is employed to generate correlator-level measurements; secondly, a deep learning method, Convolutional Neural Network (CNN), is employed to automatically extract the features and identify the signal reception type, correlators’ outputs calculated at different code phases are employed as the inputs of the CNN. Field test is carried out for assessing the performance of the proposed method, and the CNN method obtains state-of-art performance compared with the K-nearest Neighbors Algorithm (kNN) and Support Vector Machine (SVM) methods.

Determination of altitudes of the three main Ecuadorian summits through GNSS positioning

Determining the altitude of mountains is one of the most fundamental challenges for humankind. Therefore, the main objective of the study is to determine the altitudes of the three main summits in Ecuador through GNSS positioning, referring to the official vertical datum of Ecuador and the geopotential surface (Wo). The three peaks are active volcanoes named Chimborazo, Cotopaxi and Cayambe, all of which are known to be over 5700 m above sea level. Positioning GNSS was performed on the summit of the volcanoes, and the GPS leveling method was applied on nearby plates. At these summits, the correction of the potential difference between the mean sea level and Wo was used. The altitudes referred to the mean sea level were compared with other registered heights, and the results are more accurate than previous geodetic missions.

A modified between‐receiver single‐difference‐based fault detection and exclusion algorithm for real‐time kinematic positioning

With the increasing number of available satellites from multi-global navigation satellite systems (GNSS) and their applications in complicated environments, an increased number of faults in measurements are inevitable. Fault detection and exclusion (FDE) is an effective way to reject observation faults in order to guarantee the integrity of a GNSS positioning and navigation system. We propose a modified between-receiver single-differencing (SD)-based FDE algorithm for real-time kinematic (RTK) positioning. First, a modified between-receiver SD model-based FDE is proposed for testing the estimated float ambiguities. This is helpful in rejecting observations with unreliable float ambiguities and obtaining reliable double-differencing (DD) integer ambiguities, denoted as the SD float model in the sequel. Second, DD ambiguities are resolved by the DD model and used to update the previous SD float ambiguity. After that, a modified SD fix model, taking the DD ambiguities as the known parameters, is further developed to detect any unreliable or fault-resolved ambiguities. To verify the modified SD model-based FDE algorithm, a kinematic vehicle test is conducted. The fault detection test statistics of the SD float model and the SD fix model, time-to-first-fix, ambiguity-fix rate, SD residuals, and positioning performances are examined. The results show that the modified FDE method can detect and exclude fault satellites accurately and effectively, which is verified by the SD residuals of fault satellites. The ambiguity-fix rate of the proposed modified FDE algorithm is improved by approximately 3% with 70 more epochs of fixes. Regarding the 3D positioning performance, the modified proposed SD-based FDE algorithm can achieve 54.11%, 57.78%, and 73.11% improvements for standard deviation, root mean square, and mean bias, respectively, when compared with the processing strategy without the use of the proposed FDE.

Human migration-induced impacts on noise in GNSS position time series

Human migration-induced impacts on noise in GNSS position time series

Regularizing ill-posed problem of single-epoch precise GNSS positioning using an iterative procedure

Abstract This paper analyses the regularization of an ill-conditioned mathematical model in a single-epoch precise GNSS positioning. The regularization parameter (RP) is selected as a parameter that minimizes the criterion of the Mean Squared Error (MSE) function. The crucial for RP estimation is to ensure stable initial least-squares (LS) estimates to replace the unknown quadratic matrix of actual values with the LS covariance matrix. For this purpose, two different data models are proposed, and two research scenarios are formed. Two regularized LS estimations are tested against the non-regularized LS approach. The first one is the classic regularization of LS estimation. In turn, the second one is its iterative counterpart. For the LS estimator of iterative regularization, regularized bias is significantly lower while the overall accuracy is improved in the sense of MSE. The regularized variance-covariance matrix of better precision can mitigate the impact of regularized bias on integer least-squares (ILS) estimation up to some extent. Therefore, iterative LS regularization is well-designed for single-epoch integer ambiguity resolution (AR). Nevertheless, the performance of the ILS estimator is studied in the context of the probability of correct integer AR in the presence of regularized bias.