Global Navigation Satellite System Measurements Research Articles

ROTI‐Based Stochastic Model to Improve GNSS Precise Point Positioning Under Severe Geomagnetic Storm Activity

AbstractFor global navigation satellite system (GNSS), ionospheric disturbances caused by the geomagnetic storm can reduce the accuracy and reliability of precision point positioning (PPP). At present, common stochastic models in GNSS PPP, such as the elevation angle stochastic (EAS) model or carrier‐to‐noise power‐density ratio () based SIGMA‐ model, do not properly consider storm effects on GNSS measurements. To mitigate severe storm effects on GNSS PPP, this study further implements the rate of total electron content index (ROTI) parameter into the EAS model referred to as the EAS‐ROTI model. This model contains two operations. The first one is to adjust variance of GNSS measurements using ROTI observations on EAS model. The second one is to determine the ratio of the priori variance factor between pseudorange and carrier phase measurements during severe storm conditions. The performance of EAS‐ROTI model is verified by using a large number of international GNSS service stations datasets on 17 March and 23 June in 2015. Experimental results indicate that on a global scale, the EAS‐ROTI model improves the PPP accuracy in 3D direction by approximately 12.9%–14.7% compared with the EAS model, and by about 24.8%–45.9% compared with the SIGMA‐ model.

Cooperative D-GNSS Aided with Multi Attribute Decision Making Module: A Rigorous Comparative Analysis

Satellite positioning lies within the very core of numerous Intelligent Transportation Systems (ITS) and Future Internet applications. With the emergence of connected vehicles, the performance requirements of Global Navigation Satellite Systems (GNSS) are constantly pushed to their limits. To this end, Cooperative Positioning (CP) solutions have attracted attention in order to enhance the accuracy and reliability of low-cost GNSS receivers, especially in complex propagation environments. In this paper, the problem of efficient and robust CP employing low-cost GNSS receivers is investigated over critical ITS scenarios. By adopting a Cooperative-Differential GNSS (C-DGNSS) framework, the target’s vehicle receiver can obtain Position–Velocity–Time (PVT) corrections from a neighboring vehicle and update its own position in real-time. A ranking module based on multi-attribute decision-making (MADM) algorithms is proposed for the neighboring vehicle rating and optimal selection. The considered MADM techniques are simulated with various weightings, normalization techniques, and criteria associated with positioning accuracy and reliability. The obtained criteria values are experimental GNSS measurements from several low-cost receivers. A comparative and sensitivity analysis are provided by evaluating the MADM algorithms in terms of ranking performance and robustness. The positioning data time series and the numerical results are then presented, and comments are made. Scoring-based and distance-based MADM methods perform better, while L1 RMS, HDOP, and Hz std are the most critical criteria. The multi-purpose applicability of the proposed scheme, not only for land vehicles, is also discussed.

A Slowly Varying Spoofing Algorithm on Loosely Coupled GNSS/IMU Avoiding Multiple Anti-Spoofing Techniques.

When satellite navigation terminal sensors encounter malicious signal spoofing or interference, if attention is not paid to improving their anti-spoofing ability, the performance of the sensors will be seriously affected. The global navigation satellite system (GNSS) spoofing has gradually become a research hotspot of the jammer because of its great harm and high concealment. In the face of more and more sensors coupling GNSS and inertial measurement unit (IMU) to varying degrees and configuring a variety of anti-spoofing techniques to effectively detect spoofing, even if the spoofer intends to gradually pull the positioning results, if the spoofing strategy is unreasonable, the parameters of the coupled filter output and spoofing observation measurement will lose their rationality, which will lead to the spoofing being detected. To solve the above problems, in order to effectively counter the non-cooperative target sensors of assembling loosely coupled GNSS/IMU using GNSS spoofing, based on the analysis of the influence mechanism of spoofing on the positioning of loosely coupled GNSS/IMU, a slowly varying spoofing algorithm to avoid loosely coupled GNSS/IMU with multiple anti-spoofing techniques is proposed in this paper, and a measurement deviation determination method to avoid multiple anti-spoofing techniques is proposed, which can gradually pull the positioning results of the coupled system and successfully avoid the detection of anti-spoofing techniques of innovation sequence monitoring and a rationality check on parameters. Simulation experimental results show that the proposed algorithm gradually changes the positioning of loosely coupled GNSS/IMU, the north and east displacements achieve the purpose of spoofing, and error with expected offset is −0.2 m and 2.3 m, respectively. Down displacement also basically achieves the purpose of spoofing, and error with the expected offset is 13.2 m. At the same time, the spoofer avoids the detection of multiple anti-spoofing techniques, does not trigger the system alarm, and realizes the purpose of spoofing; thus, the effectiveness and high concealment of the spoofing algorithm are verified.

Estimation of vertical land motion along the south and west coast of Turkey from multi-sensor observations

This study aims to estimate Vertical Land Motion (VLM) using multi-sensor and in-situ data at the tide gauge stations (TG) located on the western and southern coasts of Turkey. The analysis of tide gauge data, used to determine sea level changes for centuries, was supported by modern global geodetic observations, and the effect of VLM on the sea level trend was determined. In this context, VLM was calculated from the relative sea level trends of eight TG's between 1998 and 2018 and the absolute sea level trends obtained from the CTOH Along-Track Sea Level Anomalies regional products (X-TRACK) and Copernicus Marine Environment Monitoring Service (CMEMS) improved altimetry data covering the same temporal period. GNSS (Global Navigation Satellite System) measurements give a solution of the point-wise information about VLM at TG's, whereas InSAR (Interferometric Synthetic Aperture Radar) solutions give spatial information about VLM that were calculated from Sentinel-1 data, which was put into service after 2014. The analyses carried out on Turkey's south and west coasts revealed an uplift trend at all TG's, whereas an insignificant subsidence trend was determined at the MNTS tide gauge station. When focusing on the MNTS station, in the period of 1998–2018 VLM from XTRACK minus TG and CMEMS minus TG were estimated as −0.7 ± 0.9 mm/yr and −0.5 ± 0.8 mm/yr, respectively and also during the period of 2014–2018 VLM from GNSS and InSAR were estimated as −0.6 ± 0.2 mm/yr and −0.3 ± 0.1 mm/yr, respectively.

Precise and robust sideslip angle estimation based on INS/GNSS integration using invariant extended Kalman filter

Sideslip angle estimation is vital to the safety and active control of autonomous vehicles. In this paper, an innovative vehicle kinematic-based sideslip angle estimation method is proposed. The method is built on multi-sensor fusion, which fuses the information of inertial measurement unit, global navigation satellite system (GNSS), and onboard sensors to continuously estimate the attitude and velocity of the vehicle and thus obtain the sideslip angle. The invariant extended Kalman filter framework is adopted and the left-invariant form is derived to implement the GNSS measurement update. In order to solve the unobservability problem when only a single GNSS receiver is equipped, the vehicle motion constraint is introduced into the filter to improve the accuracy of heading angle estimation. The method is validated by field test and the results show that the method is robust in terms of converge efficiency. Furthermore, the sideslip angle estimation accuracy is satisfactory with the average absolute error less than 0.25°, which meets the active safety control requirements for autonomous driving.

Loosely Coupled GNSS and IMU Integration for Accurate i-Boat Horizontal Navigation

Global Navigation Satellite System (GNSS) has been widely utilized as a navigation solution for a mobile vehicle, yet stand-alone GNSS is unable to achieve sufficient accuracy in some applications. For example, in aquatic environment, the accuracy position of Unmanned Surface Vehicle (USV) is affected by the wind, current, and waves dynamics. In this case, the integration of GNSS and Inertial Measurement Unit (IMU) is an approach that can be used to support USV localization to achieve an accurate navigation solution. This study integrates GNSS and IMU using Extended Kalman Filter (EKF) to process loosely coupled integration. The integration results show that the estimated x-position is 0.3058 m accurately and the y-position has an accuracy of 0.2780 m. Then, the loosely coupled integration results of EKF were compared with Unscented Kalman Filter (UKF) simulation in the previous studies. The integration of GNSS and IMU using EKF produces a higher RMSE value of 2D position and attitude angle than UKF Scenario I. However, due to the smoothing process, EKF can provide a visually smoother trajectory than UKF, although it has a significant difference from the observed trajectory. On the other hand, the linear velocity estimated by EKF shows better stability compared to UKF in both Scenario I and II.

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.

Impact assessment of the seasonal hydrological loading on geodetic movement and seismicity in Nepal Himalaya using GRACE and GNSS measurements

The Himalayan terrain is an epitome of ongoing convergence and geodetic deformation where both tectonic and non-tectonic forces prevail. In this study, the Gravity Recovery and Climate Experiment (GRACE) and Global Positioning System (GPS) datasets are used to assess the impact of seasonal loading on deformation with seismicity in Nepal. The recorded GPS data from 21 Global Navigation Satellite System (GNSS) stations during 2017–2020 are processed with respect to ITRF14 and the Indian reference frame, and the Center for Space Research (CSR) mascon RL06 during 2002–2020 is adopted to estimate the terrestrial water storage (TWS) change over the Ganga-Brahmaputra River basin. The results indicate that the hydrological loading effect or TWS change shows high negative, high positive, and moderately positive values in pre-monsoon, co-monsoon, and post-monsoon months, respectively. The detrended GPS data of both horizontal and vertical components correlate with the seasonal TWS change using the Pearson correlation coefficient at each GNSS site. In addition, the correlation coefficient has been interpolated using inverse distance weighting to investigate the regional TWS influence on geodetic displacement. In the north component, the correlation coefficient ranges from −0.6 to 0.6. At the same time, the TWS is positively correlated with geodetic displacement (0.82) in the east component, and the correlation coefficient is negative (−0.69) in the vertical component. The negative correlation signifies an inverse relationship between seasonal TWS variation and geodetic displacements. The strain rate is estimated, which shows higher negative values in pre-monsoon than in post-monsoon. Similarly, the effect of seismicity is 47.90% for pre-monsoon, 15.97% for co-monsoon, and 17.56% for post-monsoon. Thus we can infer that the seismicity decreases with the increase of seasonal hydrological loading. Furthermore, the effect of strain is much higher in pre-monsoon than in post-monsoon since the impact of co-monsoon continues to persist on a small scale in the post-monsoon season.

Investigation of determining the accuracy of spatial vectors by the satellite method in a real time mode

Abstract The study of determining the accuracy of spatial vectors by the global navigation satellite system (GNSS) in real time (RTK) was conducted. The possibility of construction of precision geodetic networks by the combined method of static and RTK GNSS measurements which correspond to the set accuracy and reach the maximum economic efficiency is investigated. A technique providing the densification of GNSS network and the use of two simultaneously operating GNSS receivers (Rover) is proposed. The research was carried out at the points of the GNSS network of Dnister Pumped Storage Power Plant (PSPP) (Ukraine). As a result of comparison of reference and measured elements of vectors, it was found that the average absolute error in determining the spatial distance of 14 vectors was 5.3 mm. Rejection of vectors with a closed horizon reduced the error to 2.1 mm. The vectors are most accurately determined from two satellite systems (GPS, GLONASS) and from a single base station. The recommended distance to the base station is within one kilometer. Increasing the accuracy by 75 % in determining the vector by the proposed method in RTK mode is also shown. As a result of a posteriori optimization of combined GNSS networks, high accuracy on the reliability of vectors determined by the method in RTK mode was confirmed. The technique can be used to construct precision networks, to carry out repeated measurements for the monitoring of large engineering structures with an open horizon.

Fault-Free Protection Level Equation for CLAS PPP-RTK and Experimental Evaluations.

Centimeter level augmentation system (CLAS) of the quasi-zenith satellite system (QZSS) is the first precise point positioning-real time kinematic (PPP-RTK) augmentation system of the global navigation satellite system (GNSS), which is currently providing services for Japan. CLAS broadcasts the state-space representation of correction messages along with integrity messages regarding satellite faults and the quality index of each correction. In other GNSS augmentation systems, such as the space-based augmentation system (SBAS) of GNSS, the quality indices of correction messages are used to generate fault-free protection levels that represent a position bound containing a true user position with a probability of missed detections. Although the protection level equations are well defined for the SBAS, a protection level equation for the CLAS PPP-RTK service has not been rigorously discussed in the literature. This paper proposes a fault-free protection level equation for the PPP-RTK methods that considers the probability of correct integer ambiguity fixes in the GNSS carrier phase measurements as well as the CLAS correction quality messages. The computed protection levels with position errors were experimentally compared by processing the GNSS measurements from the GNSS Earth Observation Network (GEONET) stations in Japan and the L6 messages from the CLAS broadcast using the virtual reference station-real time kinematic (VRS-RTK) techniques. Our results, based on the GEONET dataset spanning 7 days, showed that the computed protection levels using the proposed equations were larger than the position errors for all epochs. In the dataset, the RMS errors of the CLAS VRS-RTK position were 4.6 and 14 cm in the horizontal and vertical directions, respectively, whereas the horizontal protection levels ranged from 25 cm to 2.3 m and the vertical protection levels ranged from 50 cm to 5.2 m based on fault-free integrity risk of .

A Dual w-Test Based Quality Control Algorithm for Integrated IMU/GNSS Navigation in Urban Areas

Integration of the Global Navigation Satellite System (GNSS), with Inertial Measurement Unit (IMU) sensors to improve navigation performance, is widely used in many land-based applications. However, further application, especially in urban areas, is limited by the quality (due mainly to multipath effects) and availability of GNSS measurements, with a significant impact on performance, especially from low grade integration. To maximize the potential of GNSS measurements, this paper proposes a dual w-test-based quality control algorithm for integrated IMU/GNSS navigation in urban areas. Quality control is achieved through fault detection and exclusion (FDE) with the capability to detect simultaneous multiple faults in measurements from different satellites. The remaining fault-free GNSS measurements are fused with IMU sensor measurements to obtain the final improved state solution. The effectiveness of the algorithm is validated in a deep urban field test. Compared to the cases without fault exclusion, the results show improvements of about 24% and 30% in horizontal and vertical positioning components, respectively.

DGNSS Cooperative Positioning in Mobile Smart Devices: A Proof of Concept

Global Navigation Satellite System (GNSS) constitutes the foremost provider for geo-localization in a growing number of consumer-grade applications and services supporting urban mobility. Therefore, low-cost and ultra-low-cost, embedded GNSS receivers have become ubiquitous in mobile devices such as smartphones and consumer electronics to a large extent. However, limited sky visibility and multipath scattering induced in urban areas hinder positioning and navigation capabilities, thus threatening the quality of position estimates. This work leverages the availability of raw GNSS measurements in ultra-low-cost smartphone chipsets and the ubiquitous connectivity provided by modern, low-latency network infrastructures to enable a Cooperative Positioning (CP) framework. A Proof Of Concept is presented that aims at demonstratingdesigned to demonstrate the feasibility of a GNSS-only CP among networked smartphones embedding ultra-low-costequipped with GNSS receivers. The test campaign presented in this study assessed the feasibility of a client-serverthe approach over 4 G/LTE network connectivity. Results demonstrated an overall service availability above 80%, and an average accuracy improvement over the 40% w.r.t. to the GNSS standalone solution.

Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running.

The present study aimed to investigate the inter-unit consistency and validity of multiple 10-Hz Catapult Global Navigation Satellite System (GNSS) units in measuring straight-line sprint distances and speeds. A total of 13 participants performed one 45.72-m linear sprint at maximum effort while wearing all eight GNSS units at once. Total run distance and peak speed recorded using GNSS units during the sprint duration were extracted for analysis. Sprint time and peak speed were also obtained from video recordings as reference values. Inter-unit consistency was assessed using intraclass correlation coefficients (ICC) and standard errors of measurements (SEM). For a validity test, one-sample t-tests were performed to compare each GNSS unit’s distance with the known distance. Additionally, Wilcoxon signed-rank tests were performed to compare each unit’s peak speed with the reference peak speed measured using video analysis. Results showed poor inter-unit consistency for both distance (ICC = 0.131; SEM = 8.8 m) and speed (ICC = 0.323; SEM 1.3 m/s) measurements. For validity, most units recorded a total distance (44.50 m to 52.69 m) greater than the known distance of 45.72 m and a lower peak speed (7.25 (0.51) m/s) than the video-based reference values (7.78 (0.90) m/s). The present findings demonstrate that there exist variations in distance and speed measurements among different units of the same GNSS system during straight-line sprint running. Practitioners should be aware of the window of errors associated with GNSS measurements and interpret the results with caution. When making comparisons over a season, players should wear the same unit every time if logistically possible.

Sensitivity study of fiducial-aided navigation of Unmanned Aerial Vehicles

The possible applications and benefits of autonomous Unmanned Aerial Vehicle (UAV) use in urban areas are gaining considerable attention. Before these possibilities can be realized, it is essential that UAVs be able to navigate reliably and precisely in urban environments. The most common means of determining the location of a UAV is to utilize position measurements from Global Navigation Satellite Systems (GNSS). In urban environments, however, GNSS measurements are significantly degraded due to occlusions and multipath. This research analyzes the use of camera Line-of-Sight (LOS) measurements to self-describing fiducials as a replacement for conventional GNSS measurements. An extended Kalman filter (EKF) is developed and validated for the purpose of combining continuous measurements from an Inertial Measurement Unit (IMU) with the discrete LOS measurements to accurately estimate the states of a UAV. The sensitivity of the estimation error covariance to various system parameters is assessed, including IMU grade, fiducial placement, vehicle altitude, and image processing frequency.

Validation of regional and global ionosphere maps from GNSS measurements versus IRI2016 during different magnetic activity

Abstract This manuscript explores the divergence of the Vertical Total Electron Content (VTEC) estimated from Global Navigation Satellite System (GNSS) measurements using global, regional, and International Reference Ionosphere (IRI) models over low to high latitude regions during various magnetic activity. The VTEC is estimated using a territorial network consisting of 7 GNSS stations in Egypt and 10 GNSS stations from the International GNSS Service (IGS) Global network. The impact of magnetic activity on VTEC is investigated. Due to the deficiency of IGS receivers in north Africa and the shortage of GNSS measurements, an extra high interpolation is done to cover the deficit of data over North Africa. A MATLAB code was created to produce VTEC maps for Egypt utilizing a territorial network contrasted with global maps of VTEC, which are delivered by the Center for Orbit Determination in Europe (CODE). Thus we can have genuine VTEC maps estimated from actual GNSS measurements over any region of North Africa. A Spherical Harmonics Expansion (SHE) equation was modelled using MATLAB and called Local VTEC Model (LVTECM) to estimate VTEC values using observations of dual-frequency GNSS receivers. The VTEC calculated from GNSS measurement using LVTECM is compared with CODE VTEC results and IRI-2016 VTEC model results. The analysis of outcomes demonstrates a good convergence between VTEC from CODE and estimated from LVTECM. A strong correlation between LVTECM and CODE reaches about 96 % and 92 % in high and low magnetic activity, respectively. The most extreme contrasts are found to be 2.5 TECu and 1.3 TECu at high and low magnetic activity, respectively. The maximum discrepancies between LVTECM and IRI-2016 are 9.7 TECu and 2.3 TECu at a high and low magnetic activity. Variation in VTEC due to magnetic activity ranges from 1–5 TECu in moderate magnetic activity. The estimated VTEC from the regional network shows a 95 % correlation between the estimated VTEC from LVTECM and CODE with a maximum difference of 5.9 TECu.

Nonlinear Regression-Based GNSS Multipath Modelling in Deep Urban Area

As the necessity of location information closely related to everyday life has increased, the use of global navigation satellite systems (GNSS) has gradually increased in populated urban areas. Contrary to the high necessity and expectation of GNSS in urban areas, GNSS performance is easily degraded by multipath errors due to high-rise buildings and is very difficult to guarantee. Errors in the signals reflected by the buildings, i.e., multipath and non-line-of-sight (NLOS) errors, are the major cause of the poor accuracy in urban areas. Unlike other GNSS major error sources, the reflected signal error, which is a user-dependent error, is difficult to differentiate or model. This paper suggests training a multipath prediction model based on support vector regression to obtain a function of the elevation and azimuth angle of each satellite. To extract an unbiased multipath from the GNSS measurements, the clock error of high-elevation QZSS was estimated, and the clock offset with other constellations was also calculated. A nonlinear multipath map was generated, as a result of training with the extracted multipaths, by a Support Vector Machine, which appropriately reflected the geometry of the building near the user. The model was effective at improving the urban area positioning accuracy by 58.4% horizontally and 77.7% vertically, allowing us to achieve a 20 m accuracy level in a deep urban area, Teheran-ro, Seoul, Korea.

Line-of-sight path-following control utilizing an extended Kalman filter for estimation of speed and course over ground from GNSS positions

Path-following control systems for ships can be designed using both heading and course angle autopilots in conjunction with a proportional line-of-sight (LOS) guidance law. Ships are usually equipped with a gyrocompass from a safety perspective since magnetic compasses are susceptible to magnetic disturbances. Unfortunately, the gyrocompass is an expensive device, and smaller vessels and boats cannot afford to use this as the primary device for steering. An alternative solution is to compute the course over ground (COG) and speed over ground (SOG) from global navigation satellite systems (GNSS) and use these signals for feedback control. This article presents course autopilot design for path following and a five-state extended Kalman filter (EKF) to estimate the COG and SOG efficiently. Even though many algorithms exist for computation of the COG and SOG, it is advantageous to design an EKF since a state estimator can be extended to include other sensory systems such as Doppler Velocity Log (DVL), radar, attitude rate sensors, computer vision systems, etc. This is in contrast to proprietary systems that do not allow the user to modify the software. The convergence and accuracy of the EKF are significantly improved by using target-tracking models in combination with kinematic equations. A high-fidelity model of a MARINER class cargo ship is used in the path-following case study. From the simulation study, it can be concluded that the EKF successfully estimates the COG and the SOG from GNSS measurements during path following. The solution is remarkably robust and accurate, and when combined with a course autopilot, the need for a compass is eliminated during path following.

Kinematics of an Alpine rock glacier from multi-temporal UAV surveys and GNSS data

The quantification of rock glacier dynamics has gained increasing importance in recent years. In this study, the spatial and temporal flow patterns of perennially frozen debris in the active Gran Sometta rock glacier (Western Italian Alps) were investigated with repeated Unmanned Aerial Vehicle (UAV) surveys (2016–2019), Global Navigation Satellite System (GNSS) campaigns (2012−2020), geophysical prospections (2015) and ground surface temperature data (2014–2020).UAV data were used to generate maps of changes and elevation differences of the rock glacier surface by 3D point cloud comparison to evaluate surface lowering and accumulation processes. Horizontal velocities were quantified by an automatic image correlation technique and the results were then compared with horizontal surface velocities from GNSS measurements on selected points. The horizontal velocities estimated with the automatic method agree well with the GNSS velocities with an R2 = 0.99 and a RMSE lower than 0.07 m/y.Point cloud comparisons show surface lowering in the orographic left-hand side of the terminal part and in the central body of the rock glacier. The upper part exhibits almost absence of subsidence and any movement. This is explained by the lack of permafrost in this sector due to its overriding by the development of a small glacier during the Little Ice Age. As a result of the downslope movement, zones of surface rising occurred at the advancing front and at the moving ridge and furrow complexes. Surface velocity decreases from the orographic left to the right-hand side of the rock glacier tongue, where a thaw subsidence of up to 0.05 m/y was also observed. According to the GNSS measurements, the range of flow velocity of the rock glacier increased from 0.17–1.1 m/y in 2013 to 0.21–1.45 m/y in 2015 and then decreased until 2018 when the smallest surface velocity is detected. Since 2018, the creep velocities gradually started to increase again reaching values of 0.23 m/y up to a maximum of 1.9 m/y in the orographic left-hand side of the rock glacier tongue. This agrees with observations from other rock glaciers in the European Alps in recent decades.The complex Gran Sometta rock glacier dynamics can be explained by the heterogeneous distribution of permafrost and related subsurface perennially frozen ground which is thick enough (about 20–30 m) for permafrost creep to occur. Creep rates of the rock glacier permafrost depend also on the ground thermal regime: annual warmer surface conditions promote an acceleration of the creep rates within the rock glacier permafrost, whereas ground surface cooling causes a slight deceleration.

Forecasting GNSS Zenith Troposphere Delay by Improving GPT3 Model with Machine Learning in Antarctica

Antarctica has a significant impact on global climate change. However, to draw climate change scenarios, there is a need for meteorological data, such as water vapor content, which is scarce in Antarctica. Global navigation satellite system (GNSS) networks can play a major role in overcoming this problem as the tropospheric delay that can be derived from GNSS measurements is an important data source for monitoring the variation of water vapor content. This work intends to be a contribution for improving the estimation of the zenith tropospheric delay (ZTD) obtained with the latest global pressure–temperature (GPT3) model for Antarctica through the use of long short-term-memory (LSTM) and radial basis function (RBF) neural networks for modifying GPT3_ZTD. The forecasting ZTD model is established based on the GNSS_ZTD observations at 71 GNSS stations from 1 January 2018 to 23 October 2021. According to the autocorrelation of the bias series between GNSS_ZTD and GPT3_ZTD, we predict the LSTM_ZTD for each GNSS station for period from October 2020 to October 2021 using the LSTM day by day. Based on the bias between LSTM_ZTD and GPT3_ZTD of the training stations, the RBF is adopted to estimate the LSTM_RBF_ZTD of the verified station, where the LSTM_ZTD represents the temporal forecasting ZTD at a single station, and the LSTM_RBF_ZTD represents the predicted ZTD obtained from space. Both the daily and yearly RMSE are calculated against the reference (GNSS_ZTD), and the improvement of predicted ZTD is compared with GPT3_ZTD. The results show that the single-station LSTM_ZTD series has a good agreement with the GNSS_ZTD, and most daily RMSE values are within 20 mm. The yearly RMSE of the 65 stations ranges from 6.4 mm to 32.8 mm, with an average of 10.9 mm. The overall accuracy of the LSTM_RBF_ZTD is significantly better than that of the GPT3_ZTD, with the daily RMSE of LSTM_RBF_ZTD significantly less than 30 mm, and the yearly RMSE ranging from 5.6 mm to 50.1 mm for the 65 stations. The average yearly RMSE is 15.7 mm, which is 10.2 mm less than that of the GPT3_ZTD. The LSTM_RBF_ZTD of 62 stations is more accurate than GPT3_ZTD, with the maximum improvement reaching 76.3%. The accuracy of LSTM_RBF_ZTD is slightly inferior to GPT3_ZTD at three stations located in East Antarctica with few GNSS stations. The average improvement across the 65 stations is 39.6%.

Improving GNSS Positioning Using Neural-Network-Based Corrections

<h3>Abstract</h3> Deep neural networks (DNNs) are a promising tool for global navigation satellite system (GNSS) positioning in the presence of multipath and non-line-of-sight errors, owing to their ability to model complex errors using data. However, developing a DNN for GNSS positioning presents various challenges, such as (a) poor numerical conditioning caused by large variations in measurements and position values across the globe, (b) varying number and order within the set of measurements due to changing satellite visibility, and (c) overfitting to available data. In this work, we address the aforementioned challenges and propose an approach for GNSS positioning by applying DNN-based corrections to an initial position guess. Our DNN learns to output the position correction using the set of pseudorange residuals and satellite line-of-sight vectors as inputs. The limited variation in these input and output values improves the numerical conditioning for our DNN. We design our DNN architecture to combine information from the available GNSS measurements, which vary both in number and order, by leveraging recent advancements in set-based deep learning methods. Furthermore, we present a data augmentation strategy to reduce overfitting in the DNN by randomizing the initial position guesses. We, first, perform simulations and show an improvement in the initial positioning error when our DNN-based corrections are applied. After this, we demonstrate that our approach outperforms a weighted least squares (WLS) baseline on real-world data. Our implementation is available at github.com/Stanford-NavLab/deep_gnss.