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The spatiotemporal characteristics of the Antarctic ice sheet (AIS), as constrained by geodetic observations, provide us with a deeper understanding of the current evolution of ice mass balance. However, it still needs further in-depth research on interannual fluctuations and long-term trends of ice mass changes throughout the AIS. In this study, these two aspects were quantitatively analyzed through global positioning system (GPS) and gravity recovery and climate experiment/follow on (GRACE/GFO) over the past two decades. The nonlinear variation of GPS-inferred vertical land motion (VLM) and the influence of surface elastic load are of particular concern. The principal component analysis method is utilized to extract common mode signals from GPS time series, while correcting for various surface loads. The first principal components (PCs) accounted for 57.67%, 35.87%, 36.28%, and 36.03% of the total variances in the vertical components for GPS raw, atmospheric + nontidal oceanic (AO)-removed, AO + hydrographic model (AOH)-removed, and AO + GRACE/GFO-based load (AOG)-removed, respectively. Furthermore, the GPS vertical velocity, excluding the common mode component + AOG, yielded a median value of 0.13 mm/yr, which indicates that the retreat of ice mass has made a significant contribution to the GPS-observed VLM. In addition, the glacial isostatic adjustment (GIA) effect is found to play a key role in the large-scale VLM uplifting of the West AIS. After evaluating five different GIA models with GPS vertical velocity, we suggest that the ICE-6G_D model can more effectively correct GIA signals in GPS observations over Antarctica.

The Ordos block is an important active block in North China during the Cenozoic and modern tectonic activity. Based on a large amount of GPS observation data, an accurate and dense GPS velocity field of the Ordos block is calculated through frame transformation. The results indicate that the estimated internal principal tensile and compressive strain rates of the Ordos block are 2.7 [Formula: see text] and 1.5[Formula: see text], respectively. Additionally, the block exhibits rigidity and there is negligible counterclockwise rotation. Relative to the Ordos Basin, the Loop Fault on the north side of the Ordos Block displays a left-rotation strike-slip rate of approximately 0.9 mm/yr and a dip-slip rate of around 0.5 mm/yr, accompanied by slight extrusion. The dip slip rate of the southern section of Taihang Mountain fault system indicates that there is spreading movement in the southeastern part of the block. The southwestern Liupanshan fault also experiences extrusive motion. These block motion features and fault sliding distributions provide a reliable database for understanding the possible motion and dynamics mechanisms of the Ordos region.

Study region: The Tianshan region Study focus: Against the backdrop of ongoing global climate warming, the terrestrial water storage (TWS) changes are a complex dynamic process closely linked to climate change in the Tianshan. However, current researches in the Tianshan only focus on a specific component of TWS and are mainly analysed and discussed by using the Gravity Recovery and Climate Experiment (GRACE) observations, while the inversion results from Global Navigation Satellite System (GNSS) observations with high sensitivity and temporal resolution and their response analysis to extreme climate events are lacking. New hydrological insight for the region: The synthetic test show that (1) the GNSS stations in the Tianshan are able to recover the TWS changes in this region, and the recovery is better in the east Tianshan. The actual inversion results show that (2) the TWS changes inferred from various datasets exhibit good consistency in the spatiotemporal characteristics. Seasonal TWS changes all show a decreasing trend from western Tianshan to eastern Tianshan, but the GNSS observations show a larger magnitude than the GRACE and Global Land Data Assimilation System (GLDAS) observations. The annual phases in the southern Tianshan are about 2 months later than those in the northern Tianshan. Additionally, we also investigate the relationship between the interannual TWS changes and El Niño/Southern Oscillation (ENSO). The results show that (3) the interannual TWS changes inferred from the GNSS observations can more accurately detect hydrological anomalies induced by ENSO and better correspond to the abrupt years of spatial grid anomalies in the precipitation and atmospheric water vapor content, indicating that precipitation is a main driving factor of the TWS changes in the Tianshan. Our goal is to emphasize the significant potential of GNSS in estimating the TWS changes and to help monitor the impact of extreme climate changes on the hydrological processes.

The deformation monitoring of long-span railway bridges is significant to ensure the safety of human life and property. The interferometric synthetic aperture radar (InSAR) technology has the advantage of high accuracy in bridge deformation monitoring. This study monitored the deformation of the Ganjiang Super Bridge based on the small baseline subsets (SBAS) InSAR technology and Sentinel-1A data. We analyzed the deformation results combined with bridge structure, temperature, and riverbed sediment scouring. The results are as follows: (1) The Ganjiang Super Bridge area is stable overall, with deformation rates ranging from −15.6 mm/yr to 10.7 mm/yr (2) The settlement of the Ganjiang Super Bridge deck gradually increases from the bridge tower toward the main span, which conforms to the typical deformation pattern of a cable-stayed bridge. (3) The sediment scouring from the riverbed cause the serious settlement on the bridge's east side compared with that on the west side. (4) The bridge deformation negatively correlates with temperature, with a faster settlement at a higher temperature and a slow rebound trend at a lower temperature. The study findings can provide scientific data support for the health monitoring of long-span railway bridges.

In the context of ongoing global climate warming, researchers have observed an intricate relationship between the variability of Terrestrial Water Storage (TWS) on the Tibetan Plateau (TP) and climate anomalies. In this study, we utilize a combination of Global Navigation Satellite System (GNSS), Gravity Recovery and Climate Experiment (GRACE), and precipitation data to investigate the response of TWS on the TP to extreme climate changes, with a focus on seasonal and interannual variations. Our findings reveal that the first Common Mode Component (1st CMC) extracted from GNSS coordinate time series exhibits strong interannual fluctuations. Additionally, both GNSS and GRACE-inferred TWS demonstrate noticeable anomalous changes during the 2015/2016 period. The estimated periods of the 1st CMC of GNSS and GRACE-inferred TWS are approximately 5 and 7 years, respectively. Both reach a minimum during the extreme climate event in 2015/2016. Furthermore, we investigate the spatial variation of summer precipitation before and after the extreme drought event on the TP. It reveals that the inflection point of TWS occurs approximately 1 year prior to the drought event, suggesting a consistent spatial propagation process between precipitation and TWS. This indicates that the changes of TWS during this drought event were primarily influenced by precipitation. Finally, we evaluate the response of 12 sub-basins on the TP to the drought event that occurred in 2015/2016. The regions experiencing significant abnormal fluctuations in TWS are mainly located in inner and northeastern plateau. These regions show rapid upward changes in the TWS and demonstrate a strong spatial consistency with summer precipitation.

Abstract Quantitative understanding of the land water loading is a prerequisite to the construction of reliable tectonic deformation velocity field in the Tibetan Plateau (TP). Here, for the first time, we image the three‐dimensional crustal loading deformation velocity field of each land water component in the TP. Our results reveal that the loading signal strength of the six land water components ranks from largest to smallest as groundwater, glacier, lake, soil water, permafrost, and snow, with the maximum vertical velocity close to ±1.60 mm/yr and the maximum horizontal velocity exceeding 0.40 mm/yr. All land water components can achieve a strong enough vertical loading velocity exceeding the present‐day Global Positioning System (GPS) velocity at some sites. But for horizontal loading, apparent impacts are only from glacier, lake and groundwater, however, are very limited, with the absolute ratio of loading velocity to GPS velocity being smaller than 5% at almost all the sites.

ABSTRACTRapid urban development has led to severe land subsidence in Luoyang, Henan Province in the past few years. The current research on subsidence in urban areas of Luoyang is relatively few and early, and cannot reflect the current status of land subsidence in Luoyang. To grasp the current status of land subsidence in Luoyang and prevent the harm caused by subsidence. We used 57 Sentinel-1A Synthetic Aperture Radar (SAR) images covering Luoyang to obtain the land subsidence from April 2017 to December 2021 by using the small baseline subset interferometric SAR technique. The results show that there are several areas of land subsidence in Luoyang City, and the most serious area of subsidence is located near the highway in Jianxi District, with a cumulative subsidence of 130 mm. Land subsidence exists to varying degrees near transportation facilities (Expressway roads, railway, metro), key urban facilities (airports, high-speed railway stations) and ancient sites in Luoyang. A number of small-scale local subsidence in Luoyang is mainly related to high-intensity urban construction, and urban highway and building construction are the main causes of local land subsidence. Industrial activities are also an important factor in land subsidence in Luoyang. Groundwater and rainfall can impact land subsidence, and large amounts of rainfall during the flood season can replenish groundwater resources and slow down the rate of land subsidence. Loess foundations and large amounts of artificial fill in the city may also induce land subsidence.

Traditional inspection methods cannot quickly and accurately monitor tree barriers and safeguard the transmission lines. To solve these problems, in this study, we proposed a rapid canopy height information extraction method using optical remote sensing and LiDAR, and used UAV optical imagery with LiDAR to monitor the height of trees in a university and a high-voltage transmission line corridor in the Ningxia region. The results showed that the relative error of tree height extraction using UAV optical images was less than 5%, and the lowest relative error was 0.11%. The determination coefficient R2 between the optical image tree height extraction results and the measured tree height was 0.97, thus indicating a high correlation for both. In the field of tree barrier monitoring, the determination coefficient R2 of tree height extracted using airborne LiDAR point cloud, and canopy height model (CHM) and of the measured tree height were 0.947 and 0.931, respectively. The maximum and minimum relative error in tree height extraction performed using point cloud was 2.91% and 0.2%, respectively, with an extraction accuracy of over 95%. The experimental results demonstrated that it is feasible to use UAV optical remote sensing and LiDAR in monitoring tree barriers and tree height information extraction quickly and accurately, which is of great significance for the risk assessment and early warning of tree barriers in transmission-line corridors.

Abstract To avoid disasters caused by surface deformation in urban areas, monitoring and analyzing the mechanism of surface deformation in urban areas on a large scale and in long‐term series is mandatory. Based on PS‐InSAR technology and using 157 Sentinel‐1A images, the surface deformation in Shenzhen was monitored from 2015 to 2022, and the obtained deformation rate ranged from −22.8 to +23.2 mm/year. In this paper, analysis is presented by integrating urbanization, climate, and geology data from Shenzhen. The analysis indicates that surface deformation caused by natural factors is typically observed in regions with unique geological characteristics, such as seasonal deformation in sedimentary areas and uplift along reverse fault zones. Human factors chiefly cause subsidence, and the areas with greater subsidence are generally urbanized areas (∼10 mm/year), industrial areas (∼20 mm/year), and reclamation areas (∼15 mm/year). The deformation of the reservoir dam is subject to multiple factors. The differential subsidence of 2–8 mm/year at both ends is due to the engineering geological conditions. In comparison, the weak rotation with a linear velocity of 18 mm/year is due to hydrostatic pressure and its structural characteristics. The research results of this paper systematically explore the phenomena and the intrinsic mechanism of surface deformation in Shenzhen, providing a safety basis for urban development and emergency disaster prevention in Shenzhen.

The Earth is an elastic body, and the surface mass loading changes will lead to elastic loading deformation on the surface of the Earth. In this study, we investigated the surface seasonal mass changes and vertical crustal deformation in North China using the data obtained by the techniques of the Global Positioning System (GPS), Gravity Recovery and Climate Experiment (GRACE) and Surface Loading Models (SLMs). The seasonal annual signal and semi-annual signal obtained by the three techniques show strong correlations. The average value of the weighted root-mean-square (WRMS) of the all 30 sites is 58% after deducting the GRACE-obtained vertical deformation from the GPS-derived vertical deformation. However, the consistency of results between GPS and SLMs is not so good, with a 31% mean WRMS reduction, due to the fact that the global SLMs perform not well in North China. The GRACE-measured long-term trend is deducted from the GPS-obtained vertical rates to reveal the crustal displacement caused by the underground factors such as tectonic movement and groundwater in North China. The results show that the rates of stations HECX and TJBH are very large, more than 10 mm/yr, which suggests that the surface subsidence is caused by excessive exploitation of groundwater. Keywords: Continuous GPS measurement, GRACE measurement, Surface Loading Models, Seasonal mass changes, Vertical crustal displacement

This article presents an indoor infant monitoring system based on Android system which employs a low-cost Bluetooth Low Energy (BLE) pseudolite to accurately locate the infants in the activity indoors and timely remind the parents. It can achieve a positioning accuracy better than 2 m and effectively reduce the risk of injury to infants and young children. The performance of the proposed system, using false negative rate, false alarm rate, and alarm response time as indicators, is compared with that of the system using Quuppa as a positioning source. The experiment results show that both BLE pseudolite and Quuppa as a positioning source can basically meet the positioning accuracy for the infant monitoring system, but the BLE-pseudolite-based system provides a more cost-efficient solution.

Real-time locating and tracking Technology plays a significant role in location-based IoT applications. With the extensive installation of WiFi access points, the WiFi based indoor positioning approach has become one of the most widely used location technologies. However, due to the limitations of wireless signals, the classic WiFi-based method has become labor-intensive. Recently, the WiFi-based two-way ranging approach was introduced into the 802.11-REVmc2 protocol, which is built on a new packet type known as fine timing measurement (FTM) frame. In this work, we introduce the round-trip time measurement with clock skew and analyze the distribution of the round trip time (RTT) ranging error. A calibration method is presented to eliminate the RTT range offset at the transmitter end. We also develop an integrated ranging algorithm based on the WiFi round trip time range and received signal strength to enhance the scalability and robustness of the positioning system. The experimental results demonstrate that the proposed fusion method achieves remarkable improvement in scalability and precision in both static and dynamic tests, including outdoor and indoor environments. Compared with the classic fingerprinting approach, the performance of the system is remarkably improved, and achieves an average positioning accuracy of 1.435 m with an update rate of every 0.19 s.

Lake level change in the Tibetan Plateau is an important indicator for regional and global climate changes. We use altimeter data from Cryosat-2, SARAL, ICESat, and Jason-2 to detect lake level changes at different spatial and temporal resolutions over 2003 - 2017 (Jason-3 data in 2017 for validation). Cryosat-2’s SARIn mode provides precise water level time series over 59 lakes. SARAL’s waveforms are retracked to generate near monthly, high-quality measurements at 31 lakes. Jason-2 provides a reference for removing inter-altimeter biases, enabling coherent records over lakes with Jason-2 passes. After a decade of rise since the ICESat record of 2003, the lake levels of Nam Co, Selin Co, Ngangzi Co, and Chibuzhang Co became flat in 2014 - 2016 and started to fluctuate or decline after 2016. Such positive-flat-negative trends are consistent with the trend variations of mass change from Gravity Recovery and Climate Experiment (GRACE). SARAL detected persistent lake level declines over 2013 - 2016 in southern Tibet that may signify the onset of decadal reduced flows of the Yarlung Tsangpo and Brahmaputra River that could affect the water supply for their downstream regions in India and Bangladesh. Cryosat-2 and Jason-2 detected sudden lake level rises and falls around Zhuonai, Kusai, and Salt Lake associated with a 2011 lake outburst, which is confirmed by lake volume changes from two Landsat-7 images. With a careful processing and calibration, multiple altimeters allow for determining and cross-validating long-term and episodic lake level changes unachievable by a single altimeter.

The mass balance of water storage on the Tibetan Plateau (TP) is a complex dynamic system that has responded to recent global warming due to the special regional characteristics and geographical environment on the TP. In this study, we present global positioning system (GPS), gravity recovery and climate experiment (GRACE) and follow-on (FO) observations obtained during the 2002–2020 period to identify hydrological changes on the TP. The spatial long-term trends in the GRACE/GRACE-FO data show continuous glacier mass losses around the Himalayas and accumulated mass on the inner TP due to the increased water mass in lakes. The singular spectrum analysis (SSA) was applied for interpolation of the data gap with GRACE/GRACE-FO. We evaluated the correlation between the vertical displacements obtained from 214 continuous GPS stations and GRACE/GRACE-FO-modeled water mass loads and found a high correlation, with spatial variabilities associated with the seasonal terrestrial water storage (TWS) pattern. The common-mode component obtained from continuous GPS coordinates was decomposed using principal component analysis (PCA) and presented different periodic signals related to interannual fluctuations in hydrology and the dynamics of the inner Earth. Moreover, the various characteristics of precipitation and temperature revealed similar interannual fluctuations to those of the El Niño/Southern Oscillation. We conclude that the GPS-inferred interannual fluctuations and the corresponding GRACE/GRACE-FO-modeled hydrological loads reflect climate responses. These findings shed light on the complex role of the spatiotemporal climate and water mass balance on the TP since the beginning of the 21st century.

Modeling nonlinear vertical components of a GPS time series is critical to separating sources contributing to mass displacements. Improved vertical precision in GPS positioning at stations for velocity fields is key to resolving the mechanism of certain geophysical phenomena. In this paper, we use ensemble empirical mode decomposition (EEMD) to analyze the daily GPS time series at 89 continuous GPS stations, spanning from 2002 to 2013. EEMD decomposes a GPS time series into different intrinsic mode functions (IMFs), which are used to identify different kinds of signals and secular terms. Our study suggests that the GPS records contain not only the well-known signals (such as semi-annual and annual signals) but also the seldom-noted quasi-biennial oscillations (QBS). The quasi-biennial signals are explained by modeled loadings of atmosphere, non-tidal and hydrology that deform the surface around the GPS stations. In addition, the loadings derived from GRACE gravity changes are also consistent with the quasi-biennial deformations derived from the GPS observations. By removing the modeled components, the weighted root-mean-square (WRMS) variation of the GPS time series is reduced by 7.1% to 42.3%, and especially, after removing the seasonal and QBO signals, the average improvement percentages for seasonal and QBO signals are 25.6% and 7.5%, respectively, suggesting that it is significant to consider the QBS signals in the GPS records to improve the observed vertical deformations.

Surface and deep potential geophysical signals respond to the spatial redistribution of global mass variations, which may be monitored by geodetic observations. In this study, we analyze dense Global Positioning System (GPS) time series in the Eastern Tibetan Plateau using principal component analysis (PCA) and wavelet time-frequency spectra. The oscillations of interannual and residual signals are clearly identified in the common mode component (CMC) decomposed from the dense GPS time series from 2000 to 2018. The newly developed spherical harmonic coefficients of the Gravity Recovery and Climate Experiment Release-06 (GRACE RL06) are adopted to estimate the seasonal and interannual patterns in this region, revealing hydrologic and atmospheric/nontidal ocean loads. We stack the averaged elastic GRACE-derived loading displacements to identify the potential physical significance of the CMC in the GPS time series. Interannual nonlinear signals with a period of ~3 to ~4 years in the CMC (the scaled principal components from PC1 to PC3) are found to be predominantly related to hydrologic loading displacements, which respond to signals (El Niño/La Niña) of global climate change. We find an obvious signal with a period of ~6 yr on the vertical component that could be caused by mantle-inner core gravity coupling. Moreover, we evaluate the CMC’s effect on the GPS-derived velocities and confirm that removing the CMC can improve the recognition of nontectonic crustal deformation, especially on the vertical component. Furthermore, the effects of the CMC on the three-dimensional velocity and uncertainty are presented to reveal the significant crustal deformation and dynamic processes of the Eastern Tibetan Plateau.

The presence of None-line-of-sight (NLOS) is one of the major challenging issues in time of arrival (TOA) based source localization, especially for the sparse anchor scenarios. Sparse anchors can reduce the system deployment cost, so this has become increasingly popular in the source location. However, fewer anchors bring new challenges to ensure localization precision and reliability, especially in NLOS environments. The maximum likelihood (ML) estimation is the most popular location estimator for its simplicity and efficiency, while it becomes extremely difficult to reliably identify the NLOS measurements when the redundant observations are not enough. In this study, we proposed an NLOS detection algorithm called misclosure check (MC) to overcome this issue, which intends to provide a more reliable location in the sparse anchor environment. The MC algorithm checks the misclosure of different triangles and then obtains the possible NLOS from these misclosures. By forming multiple misclosure conditions, the MC algorithm can identify NLOS measurements reliably, even in a sparse anchor environment. The performance of the MC algorithm is evaluated in a typical sparse anchor environment and the results indicate that the MC algorithm achieves promising NLOS identification capacity without abundant redundant measurements. The real data test also confirmed that the MC algorithm achieves better position precision than other three robust location estimators in an NLOS environment since it can correctly identify more NLOS measurements.

Reliable and continuous navigation solutions are essential for high-accuracy location-based services. Currently, the real-time kinematic (RTK) based Global Positioning System (GPS) is widely utilized to satisfy such requirements. However, RTK’s accuracy and continuity are limited by the insufficient number of the visible satellites and the increasing length of base-lines between reference-stations and rovers. Recently, benefiting from the development of precise point positioning (PPP) and BeiDou satellite navigation systems (BDS), the issues existing in GPS RTK can be mitigated by using GPS and BDS together. However, the visible satellite number of GPS + BDS may decrease in dynamic environments. Therefore, the inertial navigation system (INS) is adopted to bridge GPS + BDS PPP solutions during signal outage periods. Meanwhile, because the quality of BDS geosynchronous Earth orbit (GEO) satellites is much lower than that of inclined geo-synchronous orbit (IGSO) satellites, the predicted observation residual based robust extended Kalman filter (R-EKF) is adopted to adjust the weight of GEO and IGSO data. In this paper, the mathematical model of the R-EKF aided GEO/IGSO/GPS PPP/INS tight integration, which uses the raw observations of GPS + BDS, is presented. Then, the influences of GEO, IGSO, INS, and R-EKF on PPP are evaluated by processing land-borne vehicle data. Results indicate that (1) both GEO and IGSO can provide accuracy improvement on GPS PPP; however, the contribution of IGSO is much more visible than that of GEO; (2) PPP’s accuracy and stability can be further improved by using INS; (3) the R-EKF is helpful to adjust the weight of GEO and IGSO in the GEO/IGSO/GPS PPP/INS tight integration and provide significantly higher positioning accuracy.

In this study, we employed multitemporal InSAR (Interferometric Synthetic Aperture Radar) (MT-InSAR) to detect spatial and temporal ground deformations over the whole Tianjin region in the North China Plain area. Twenty-five ascending Sentinel-1A terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR) scenes covering this area, acquired from 9 January 2016 to 8 June 2017, were processed using InSAR time series analysis. The deformation results derived from Sentinel-1A MT-InSAR were validated with continuously operating reference stations (CORS) at four sites and four stations of the Crustal Movement Observation Network of China (CMONOC). The overall results show good agreement, demonstrating the suitability of applying Doris with Sentinel-1A data to high-resolution monitoring of surface deformation. Significant deformation variations have been observed in different parts of Tianjin. These gradually increased from the central part of the metropolitan area to the nearby suburbs. The deformation rate of the main urban area is well-balanced and it is also relatively linear, with uplifting rates ranging from 0 to 20 mm/yr. However, due to the diversity of the geological conditions and anthropogenic activities, remarkable signs of subsidence were found in several parts of Tianjin. In particular, the south-western part of Wuqing District and western part of Beichen District showed subsidence rates of up to −136 mm/yr. We also found that, in addition to groundwater over-exploitation and lithological characteristics, additional factors also influence ground subsidence, including dynamic loads (e.g., railways), static loads (e.g., urban construction), and groundwater recharging.

Pedestrian walking speeds (PWS) can be used as a “body speedometer” to reveal health status information of pedestrians and positioning indoors with other locating methods. This paper proposes a pose awareness solution for estimating pedestrian walking speeds using the sensors built in smartphones. The smartphone usage pose is identified by using a machine learning approach based on data from multiple sensors. The data are then coupled tightly with an adaptive step detection solution to estimate the pedestrian walking speed. Field tests were carried out to verify the advantages of the proposed algorithms compared to existing solutions. The test results demonstrated that the features extracted from the data of the smartphone built-in sensors clearly reveal the characteristics of the pose pattern, with overall accuracy of 98.85% and a kappa statistic of 98.46%. The proposed walking speed estimation solution, running in real-time on a commercial smartphone, performed well, with a mean absolute error of 0.061 m/s, under a challenging walking process combining various usage poses including texting, calling, swinging, and in-pocket modes.

A low Earth orbiter (LEO)-based navigation signal augmentation system is considered as a complementary of current global navigation satellite systems (GNSS), which can accelerate precise positioning convergence, strengthen the signal power, and improve signal quality. Wuhan University is dedicated to LEO-based navigation signal augmentation research and launched one scientific experimental satellite named Luojia-1A. The satellite is capable of broadcasting dual-frequency band ranging signals over China. The initial performance of the Luojia-1A satellite navigation augmentation system is assessed in this study. The ground tests indicate that the phase noise of the oscillator is sufficiently low to support the intended applications. The field ranging tests achieve 2.6 m and 0.013 m ranging precision for the pseudorange and carrier phase measurements, respectively. The in-orbit test shows that the internal precision of the ephemeris is approximate 0.1 m and the clock stability is 3 × 10−10. The pseudorange and carrier phase measurement noise evaluated from the geometry-free combination is about 3.3 m and 1.8 cm. Overall, the Luojia-1A navigation augmentation system is capable of providing useable LEO navigation augmentation signals with the empirical user equivalent ranging error (UERE) no worse than 3.6 m, which can be integrated with existing GNSS to improve the real-time navigation performance.

In Global navigation satellite system (GNSS) data processing, integer ambiguity acceptance test is considered as a challenging problem. A number of ambiguity acceptance tests have been proposed from different perspective and then unified into the integer aperture estimation (IA) framework. Among all the IA estimators, the optimal integer aperture (OIA) achieves the highest success rate with the fixed failure rate tolerance. However, the OIA is of less practical appealing due to its high computation complexity. On the other hand, the popular discrimination tests employ only two integer candidates, which are the essential reason for their sub-optimality. In this study, a generalized difference test (GDT) is proposed to exploit the benefit of including three or more integer candidates to improve their performance from theoretical perspective. The simulation results indicate that the third best integer candidates contribute to more than 70% success rate improvement for integer bootstrapping success rate higher than 0.8 case. Therefore, the GDT with three integer candidates (GDT3) achieves a good trade-off between the performance and computation burden. The threshold function is also applied for rapid determination of the fixed failure rate (FF)-threshold for GDT3. The performance improvement of GDT3 is validated with real GNSS data set. The numerical results indicate that GDT3 achieves higher empirical success rate while the empirical failure rate remains comparable. In a 20 km baseline test, the success rate GDT3 increase 7% with almost the same empirical failure rate.

The solid Earth deforms elastically in response to variations of surface atmosphere, hydrology, and ice/glacier mass loads. Continuous geodetic observations by Global Positioning System (CGPS) stations and Gravity Recovery and Climate Experiment (GRACE) record such deformations to estimate seasonal and secular mass changes. In this paper, we present the seasonal variation of the surface mass changes and the crustal vertical deformation in the South China Block (SCB) identified by GPS and GRACE observations with records spanning from 1999 to 2016. We used 33 CGPS stations to construct a time series of coordinate changes, which are decomposed by empirical orthogonal functions (EOFs) in SCB. The average weighted root-mean-square (WRMS) reduction is 38% when we subtract GRACE-modeled vertical displacements from GPS time series. The first common mode shows clear seasonal changes, indicating seasonal surface mass re-distribution in and around the South China Block. The correlation between GRACE and GPS time series is analyzed which provides a reference for further improvement of the seasonal variation of CGPS time series. The results of the GRACE observations inversion are the surface deformations caused by the surface mass change load at a rate of about −0.4 to −0.8 mm/year, which is used to improve the long-term trend of non-tectonic loads of the GPS vertical velocity field to further explain the crustal tectonic movement in the SCB and surroundings.

After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to “see” which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for indoor scene model training and communication with an Android client. To evaluate the performance, comparison experiments are conducted and the results demonstrate that a positioning accuracy of 1.32 m at 95% is achievable with the proposed solution. Both positioning accuracy and robustness are enhanced compared to approaches without scene constraint including commercial products such as IndoorAtlas.

The Terrain Observation with Progressive Scans (TOPS) acquisition mode of Sentinel-1A provides a wide coverage per acquisition and features a repeat cycle of 12 days, making this acquisition mode attractive for surface subsidence monitoring. A few studies have analyzed wide-coverage surface subsidence of Wuhan based on Sentinel-1A data. In this study, we investigated wide-area surface subsidence characteristics in Wuhan using 15 Sentinel-1A TOPS Synthetic Aperture Radar (SAR) images acquired from 11 April 2015 to 29 April 2016 with the Small Baseline Subset Interferometric SAR (SBAS InSAR) technique. The Sentinel-1A SBAS InSAR results were validated by 110 leveling points at an accuracy of 6 mm/year. Based on the verified SBAS InSAR results, prominent uneven subsidence patterns were identified in Wuhan. Specifically, annual average subsidence rates ranged from −82 mm/year to 18 mm/year in Wuhan, and maximum subsidence rate was detected in Houhu areas. Surface subsidence time series presented nonlinear subsidence with pronounced seasonal variations. Comparative analysis of surface subsidence and influencing factors (i.e., urban construction, precipitation, industrial development, carbonate karstification and water level changes in Yangtze River) indicated a relatively high spatial correlation between locations of subsidence bowl and those of engineering construction and industrial areas. Seasonal variations in subsidence were correlated with water level changes and precipitation. Surface subsidence in Wuhan was mainly attributed to anthropogenic activities, compressibility of soil layer, carbonate karstification, and groundwater overexploitation. Finally, the spatial-temporal characteristics of wide-area surface subsidence and the relationship between surface subsidence and influencing factors in Wuhan were determined.

Dislocation is one of the major challenges in unmanned aerial vehicle (UAV) image stitching. In this paper, we propose a new algorithm for seamlessly stitching UAV images based on a dynamic programming approach. Our solution consists of two steps: Firstly, an image matching algorithm is used to correct the images so that they are in the same coordinate system. Secondly, a new dynamic programming algorithm is developed based on the concept of a stereo dual-channel energy accumulation. A new energy aggregation and traversal strategy is adopted in our solution, which can find a more optimal seam line for image stitching. Our algorithm overcomes the theoretical limitation of the classical Duplaquet algorithm. Experiments show that the algorithm can effectively solve the dislocation problem in UAV image stitching, especially for the cases in dense urban areas. Our solution is also direction-independent, which has better adaptability and robustness for stitching images.

Surface vertical deformation includes the Earth’s elastic response to mass loading on or near the surface. Continuous Global Positioning System (CGPS) stations record such deformations to estimate seasonal and secular mass changes. We used 41 CGPS stations to construct a time series of coordinate changes, which are decomposed by empirical orthogonal functions (EOFs), in northeastern Tibet. The first common mode shows clear seasonal changes, indicating seasonal surface mass re-distribution around northeastern Tibet. The GPS-derived result is then assessed in terms of the mass changes observed in northeastern Tibet. The GPS-derived common mode vertical change and the stacked Gravity Recovery and Climate Experiment (GRACE) mass change are consistent, suggesting that the seasonal surface mass variation is caused by changes in the hydrological, atmospheric and non-tidal ocean loads. The annual peak-to-peak surface mass changes derived from GPS and GRACE results show seasonal oscillations in mass loads, and the corresponding amplitudes are between 3 and 35 mm/year. There is an apparent gradually increasing gravity between 0.1 and 0.9 μGal/year in northeast Tibet. Crustal vertical deformation is determined after eliminating the surface load effects from GRACE, without considering Glacial Isostatic Adjustment (GIA) contribution. It reveals crustal uplift around northeastern Tibet from the corrected GPS vertical velocity. The unusual uplift of the Longmen Shan fault indicates tectonically sophisticated processes in northeastern Tibet.

Modern geodetic technologies, including high-precision ground-based gravity measurements, satellite gravimetry, satellite altimetry, Global Navigation Satellite Systems (GNSS), and Interferometric Synthetic Aperture Radar(InSAR), offer a wealth of observations for monitoring global hydrological processes with exceptional accuracy and spatio-temporal resolutions. Mass redistribution and Earth's surface deformation over land related to global and regional water cycling can be inferred from modern gravimetry, altimetry, GNSS, and InSAR techniques. Hydrogeodesy becomes an emerging field of geodesy aiming to analyze the changes of water in the Earth system. The paper introduces the China's advances in hydrogeodesy in recent years. It brings together multiple geodetic teams' work from China, showcasing the application of modern geodetic technologies in the field of hydrology, including research on terrestrial water storage, groundwater storage, glaciers/ice sheets, and reservoir water storage. [ABSTRACT FROM AUTHOR]