Monitoring of Coastal Subsidence Changes Based on GNSS Positioning and GNSS-IR

Coastal subsidence monitoring typically employs Global Navigation Satellite System(GNSS)positioning technology.This method provides information only about subsidence below the station base.Sediments in coastal areas tend to accumulate quickly,and subsidence can change significantly due to compaction and alluvium.Therefore,monitoring subsidence above the base is essential to obtain overall coastal subsidence.A new technology called GNSS-Interferometric Reflectometry(GNSS-IR)has been recently developed,which can utilize multipath effects to monitor reflector height.Since the base of the GNSS station is deep and the base length remains constant,the height changes measured by the GNSS-IR technology can reflect subsidence above the base.Accordingly,this paper employs GNSS-IR technology to measure subsidence changes above the base.Additionally,GNSS positioning technology is used to obtain subsidence changes below the base,and the overall subsidence change is then calculated using both GNSS-IR and GNSS positioning technology.The Mississippi River Delta,known for its significant sediment thickness,was selected as the study area,and data from FSHS,GRIS,and MSIN stations was analyzed.The results demonstrate that GNSS-IR can be used to measure the subsidence rate above the base,and the corrected overall subsidence rate is equivalent to the relative sea level rise rate.

Crowdsourcing RTK:a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution(AR)in precise positioning.However,traditional real-time precise positioning frameworks(i.e.,NRTK and PPP-RTK)depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network.This results in limited public appeal.With the mass production of autonomous driving vehicles,more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps.In this study,we propose a new GNSS positioning framework that relies on dual base stations,massive vehicle GNSS data,and crowdsourced atmospheric delay correction maps(CAM).The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way.Specifically,the map consists of between-station single-differenced ionospheric and tropospheric delays.We introduce the whole framework of CAM initialization for individual vehicles,on-cloud CAM maintenance,and CAM-augmented user-end positioning.The map data are collected and preprocessed in vehicles.Then,the crowdsourced data are uploaded to a cloud server.The massive data from multiple vehicles are merged in the cloud to update the CAM in time.Finally,the CAM will augment the user positioning performance.This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other.We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales.We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.

Improved GNSS Cooperation Positioning Algorithm for Indoor Localization

For situations such as indoor and underground parking lots in which satellite signals are obstructed,GNSS cooperative positioning can be used to achieve highprecision positioning with the assistance of cooperative nodes.Here we study the cooperative positioning of two static nodes,node 1 is placed on the roof of the building and the satellite observation is ideal,node 2 is placed on the indoor windowsill where the occlusion situation is more serious,we mainly study how to locate node 2 with the assistance of node 1.Firstly,the two cooperative nodes are located with pseudo-range single point positioning,and the positioning performance of cooperative node is analyzed,therefore the information of pseudo-range and position of node 1 is obtained.Secondly,the distance between cooperative nodes is obtained by using the baseline method with double-difference carrier phase.Finally,the cooperative location algorithms are studied.The Extended Kalman Filtering(EKF),Unscented Kalman Filtering(UKF)and Particle Filtering(PF)are used to fuse the pseudo-range,ranging information and location information respectively.Due to the mutual influences among the cooperative nodes in cooperative positioning,the EKF,UKF and PF algorithms are improved by resetting the error covariance matrix of the cooperative nodes at each update time.Experimental results show that after being improved,the influence between the cooperative nodes becomes smaller,and the positioning performance of the nodes is better than before.

Reference satellite selection method for GNSS high-precision relative positioning

Selecting the optimal reference satellite is an important component of high-precision relat/ve positioning because the reference satellite directly influences the strength of the normal equation. The reference satellite selection methods based on elevation and positional dilution of precision （PDOP） value were compared. Results show that all the above methods cannot select the optimal reference satellite. We introduce condition number of the design matrix in the reference satellite selection method to improve structure of the normal equation, because condition number can indicate the ill condition of the normal equation. The experimental results show that the new method can improve positioning accuracy and reliability in precise relative positioning.

A graph deep learning method for landslide displacement prediction based on global navigation satellite system positioning

The accurate prediction of displacement is crucial for landslide deformation monitoring and early warning.This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate landslide displacement prediction method that relies on graph deep learning and Global Navigation Satellite System(GNSS)positioning.First model the graph structure of the monitoring system based on the engineering positions of the GNSS monitoring points and build the adjacent matrix of graph nodes.Then construct the historical and predicted time series feature matrixes using the processed temporal data including GNSS displacement,rainfall,groundwater table and soil moisture content and the graph structure.Last introduce the state-of-the-art graph deep learning GTS(Graph for Time Series)model to improve the accuracy and reliability of landslide displacement prediction which utilizes the temporal-spatial dependency of the monitoring system.This approach outperforms previous studies that only learned temporal features from a single monitoring point and maximally weighs the prediction performance and the priori graph of the monitoring system.The proposed method performs better than SVM,XGBoost,LSTM and DCRNN models in terms of RMSE(1.35 mm),MAE(1.14 mm)and MAPE(0.25)evaluation metrics,which is provided to be effective in future landslide failure early warning.

Structure and performance analysis of fusion positioning system with a single 5G station and a single GNSS satellite

NaGlobal vigation Satellite System(GNSS)positioning technology is widely used for its high precision,global,and all-weather service.However,in complex environments such as urban canyons,GNSS performance is often degraded due to signal occlusion and even fails to achieve positioning due to the insufficient visible satellites.Because of the characteristics of large band-width,low latency,and high Base Station(BS)density,the fifth-Generation mobile communication(5G)technology has gradually become a trend for positioning in cities while offering traditional communication service.To supply the communication demands of the User Equipment(UE),only one BS is usually considered to establish a connection with the UE during the BS construction.However,the positioning accuracy with a single BS in urban canyons will be significantly reduced.To further improve the positioning accuracy in such extreme scenarios,this paper proposes a simplified 5G/GNSS fusion positioning system architecture using observations from only a 5G BS and a GNSS satellite.In this system,the GNSS receiver is mounted on the 5G BS,and the measurements provided by the receiver are used to form the differential code and complete the position estimation.The positioning mathematical models of the system based on the original code and differential code are derived.Then,the impacts of the measurements noise and the time synchronization error on the positioning accuracy are analyzed theoretically.Finally,the positioning performance is investigated by a set of simulation experiments.Numerical results show that under the existing 5G measurement noise and 2 m’s code measurement noise,the improvement of the differential code based fusion positioning compared with the 5G-only positioning is more than 32%,which is also about 6%higher than the original code based fusion positioning.Besides,this improvement is not affected by the time synchronization error between the BS and the GNSS satellite.

Global modeling 2^(nd)-order ionospheric delay and its effects on GNSS precise positioning

Ionospheric delay is one of the major error sources in GNSS navigation and positioning.Nowadays,the dual-frequency technique is the most widely used in ionospheric refraction correction.However,dual-frequency measurements can only eliminate the first-order term of ionospheric delay,while the effect of the second-order term on GNSS observations may be several centimeters.In this paper,two models,the International Reference Ionosphere (IRI) 2007 and International Geomagnetic Reference Field (IGRF) 11 are used to estimate the second-order term through the integral calculation method.Besides,the simplified single layer ionosphere model in a dipole moment approximation for the earth magnetic field is used.Since the traditional integral calculation method requires large calculation load and takes much time,it is not convenient for practical use.Additionally,although the simplified single layer ionosphere model is simple to implement,it results in larger errors.In this study,second-order term ionospheric correction formula proposed by Hoque (2007) is improved for estimating the second-order term at a global scale.Thus,it is more practicable to estimate the second-order term.More importantly,its results have a higher precision of the sub-millimeter level for a global scale in normal conditions.Compared with Hoque's original regional correction model,which calculates coefficients through polynomial fitting of elevation and latitudes,this study proposes a piece-wise look-up table and interpolation technique to modify Hoque model.Through utilizing a table file,the modified Hoque model can be conveniently implemented in an engineering software package,like as PANDA in this study.Through applying the proposed scheme for the second-order ionospheric correction into GNSS precise positioning in both PPP daily and epoch solutions,the results have shown south-shift characteristics in daily solution at a global scale and periodic change with VTEC daily variation in epoch positioning solution.

Vulnerabilities and integrity of precise point positioning for intelligent transport systems:overview and analysis

The implementation of Intelligent Transport System (ITS) technology is expected to significantly improve road safety and traffic efficiency. One of the key components of ITS is precise vehicle positioning. Positioning with decimetre to sub-metre accuracy is a fundamental capability for self-driving, and other automated applications. Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) is an attractive positioning approach for ITS due to its relatively low-cost and flexibility. However, GNSS PPP is vulnerable to several effects, especially those caused by the challenging urban environments, where the ITS technology is most likely needed. To meet the high integrity requirements of ITS applications, it is necessary to carefully analyse potential faults and failures of PPP and to study relevant integrity monitoring methods. In this paper an overview of vulnerabilities of GNSS PPP is presented to identify the faults that need to be monitored when developing PPP integrity monitoring methods. These vulnerabilities are categorised into different groups according to their impact and error sources to assist integrity fault analysis, which is demonstrated with Failure Modes and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) methods. The main vulnerabilities are discussed in detail, along with their causes, characteristics, impact on users, and related mitigation methods. In addition, research on integrity monitoring methods used for accounting for the threats and faults in PPP for ITS applications is briefly reviewed. Both system-level (network-end) and user-level (user-end) integrity monitoring approaches for PPP are briefly discussed, focusing on their development and the challenges in urban scenarios. Some open issues, on which further efforts should focus, are also identified.