This study developed a low-Earth orbit (LEO) enhanced GPS, Galileo, and Beidou-3 navigation satellite system positioning using an undifferenced and uncombined (UDUC) precise point positioning-ambiguity resolution (PPP-AR) model, evaluating its performance over 7575 km and 145 km reference networks. Atmospheric delays were interpolated from reference stations to users via the modified linear combined model (MLCM) to constrain the UDUC PPP. The results demonstrate that, in both static and kinematic positioning, LEO satellites significantly enhance positioning and AR performance, notably improving convergence speed. While regional atmospheric delay augmentation also yields significant gains, its efficacy decreases with larger network scales, leading the 145 km network to underperform the 75 km network. The study concludes that LEO enhancement combined with atmospheric delay constraints is a powerful tool for high-precision positioning, with network scale being also a factor for optimal performance This study presents the first comprehensive evaluation of the synergistic effects between LEO fixed solutions enhancement and regional atmospheric delay constraints in UDUC static and kinematic PPP-AR, providing novel insights for network scale optimization.

In response to the persistent limitations of multi-system precise point positioning (PPP) in terms of convergence speed, ambiguity fixing rate, and especially positioning performance in kinematic scenarios, this study proposes a low-Earth orbit (LEO) fixed solution-enhanced positioning model, aiming to significantly improve both the positioning performance and ambiguity resolution (AR) performance of PPP. Based on the undifferenced and uncombined (UDUC) PPP framework, GPS, Galileo, and BDS-3 are enhanced using LEO satellites under both static and kinematic positioning modes, and performs AR for the satellites of the participating systems. Compared with the float solution, AR improves both positioning accuracy and convergence speed. After ambiguity resolution, the convergence time for static and kinematic positioning in the three-dimensional (3-D) direction is shortened by 24% and 8%, respectively, and the positioning accuracy is improved by 40% and 30%, respectively. Compared with single-system solutions, the inclusion of more systems improves positioning accuracy, convergence speed, and AR performance. In particular, the enhancement of LEO satellites significantly accelerates the convergence. The enhancement with LEO satellites shortens the 3-D convergence time for static and kinematic positioning by 88% and 91%, improves the positioning accuracy by 35% and 50%, increases the ambiguity fixing rate by 17% and 52%, and shortens the time to first fix (TTFF) by 72% and 79%, respectively. This study confirms that LEO satellite enhancement can effectively advance PPP technology toward rapid ambiguity resolution and real-time high-precision positioning.

Nowdays unmanned aerial vehicles (UAVs) are widely applied in agricultural tasks such as pesticide spraying, fertilization and crop condition monitoring. Computer vision based navigation route recognition technology serves as a core guarantee for the automated operations. Compared with field crop environments, tea gardens are more complex: tea tree height variations and weed interference often cause inaccurate navigation line identification. Moreover, constrained UAV onboard resources demand high model efficiency and lightweight performance. To address these challenges, this study proposes a tea plant row navigation line recognition method based on the channel attention-embedded network (CA-ENet). The method constructs a HybridAtt module, introduces a channel attention (CA) mechanism to enhance tea plant row key feature representation, employs an atrous spatial pyramid pooling (ASPP) module to improve complex scene comprehension, and optimizes the upsampling module to reduce load and accelerate inference. After extracting feature points of tea plant rows via contour detection and contour center point extraction algorithms, the least squares method (LSM) is adopted to fit the final navigation lines. Experimental results demonstrate that the CA-ENet model achieves a mean intersection over union (mIoU) of 89.21% and a mean pixel accuracy (MPA) of 94.24%. In terms of efficiency and lightweight performance, it maintains a parameter size of only 0.72 MB, a segmentation speed of 25.7 frames/s, and a navigation line fitting speed of 42.3 frames/s. Its overall performance outperforms five comparative models including TransUNet and PSPNet, making it highly suitable for real-time autonomous navigation of UAVs in dynamic agricultural scenarios.

Accurate representation of the topside ionosphere is essential for GNSS ionospheric delay correction, but the effective scale height H used in NeQuick remains weakly constrained at high altitudes because of the limited availability of direct electron density observations. In this study, ISIS-2 topside sounder profiles from 1976 to 1979 are used to develop a continuity-constrained height-segmented correction for the NeQuick topside formulation. The correction is evaluated against alternative correction schemes and applied to NeQuick for topside electron density reconstruction and TEC estimation under different solar activity conditions. The experimental results show that the upper topside above 800 km exhibits clearer nonlinear scale-height behavior under lower solar activity. The quadratic upper-topside model reduces the MRE from 42.3 to 38.4

The eruption of large volcanoes may potentially disturb the upper atmosphere, causing disturbances in ionospheric plasma and triggering irregular changes in the Total Electron Content (TEC). The GNSS ionospheric inversion method has been used to analyze ionospheric anomalies caused by the volcano, while it was limited to the spatial distribution of ground-based GNSS. The joint observation with space-based GNSS can further improve the spatio-temporal distribution of ionospheric monitoring. The Tonga volcano erupted on January 15th, 2022, is the largest volcanic eruption since the beginning of the 21st century and generated intense acoustic, atmospheric gravity waves and ionospheric disturbance. In this paper, we used the GIM products, ground-based GNSS station data around the volcanic eruption point and COSMIC occultation data to analyze the short-term TEC change. The experiments showed that: the average TEC of GIM grid data on eruption day dropped by 7.5 TECu compared to the last day which was substantially larger than other three days. The TEC extracted from nearby tracking stations was smaller than GIM and the difference was inversely proportional to the distance between the station and the volcanic eruption site. The average daily ionospheric TEC obtained from the nearest TONG station was the biggest, as much as 3.44 TECu lower. The peak electron density of COSMIC occultation data gradually diminished and then recovered with TEC about 7.32 TECu lower than GIM products. The experments showed that there was a good consistency in the TEC obtained from space-based and ground-based GNSS, the Tonga volcano produced a huge TEC change over 10 TECu or even bigger. As a global grid product, GIM underestimated ionospheric changes due to its limited spatial resolution, which needs further improvement. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This study investigates the complex tectonic interactions and crustal deformation within the Weihe Basin and its surrounding regions, encompassing the northeastern Tibetan Plateau, Ordos Craton, and Qinling Orogenic Belt. By conducting a detailed analysis of Global Navigation Satellite System (GNSS) data and employing a refined tectonic model, we explore relative motion patterns and fault activities in the area. Our findings highlight nuanced movement patterns, with a clockwise rotation observed in the western and central parts of basin, contrasting with an anticlockwise rotation in the eastern part. Secondary block motion decreases from west to east, with the western region showing southeastwards motion and the eastern region exhibiting subtle eastwards deflection. Fault activities within the Weihe Basin generally feature low slip rates, often below 1 mm a-1. Intriguingly, faults in the northern basin predominantly exhibit dextral and extensional movement, while those in the southern region display sinistral and compressive movement. The Weihe fault is identified as a critical boundary between the Ordos block and the Qinling Orogenic Belt. This study offers valuable insights into the tectonic complexities of the Weihe Basin, enhancing our understanding of its kinematic behaviour.

This article has summarized the shortcomings and deficiencies in the construction and operation of the earthquake monitoring station network of China, analyzes the trend of modernization development of the monitoring station network, and proposes measures and suggestions for upgrading and replacing the earthquake monitoring network of China from the perspective of a new development philosophy. It has also detailed the design and implementation of the national disaster prevention project.

With the continuous advancement of LiDAR technology, solid-state LiDAR, with its low cost and unique scanning mode, shows great potential in measurement applications. However, in large-scale environments, the SLAM algorithm LOAM-Livox for solid-state LiDAR often accumulates registration errors, limiting its applicability. To address this, we propose a segment-based SLAM registration optimization algorithm that combines Normal Distributions Transform (NDT) and Point-to-Line Iterative Closest Point (PL-ICP). This algorithm divides the entire data processing into segments, performs SLAM independently on each segment, and registers overlapping areas between adjacent segments to minimize error accumulation. Experiments on both public and self-collected datasets demonstrate that the proposed NDT + PL-ICP optimization algorithm significantly improves the accuracy of mobile mapping with solid-state LiDAR. This approach effectively resolves the error accumulation issue in SLAM, confirming its effectiveness and practicality in real-world applications.

Groundwater withdrawal and recharge lead to changes in terrestrial hydrological loads, which in turn cause surface deformation. Based on poroelastic response and elastic loading theory, the 24 Global Positioning System (GPS) stations on the North China Plain (NCP) and the Gravity Recovery and Climate Experiment mission and its follow-on (GRACE/GRACE-FO) are first integrated to quantify the spatial–temporal changes in surface deformation and groundwater storage (GWS) during 2011–2022. The results show that the trends of GWS in the three periods of 2011–2017, 2018–2020, and 2021–2022 were − 2.56 ± 0.33 mm/yr, − 4.72 ± 1.74 mm/yr, and 11.76 ± 4.18 mm/yr, respectively. Most of the GPS stations showed a significant negative correlation between GWS and surface deformation under the elastic loading theory. In 2021, surface subsidence of more than 5 mm was experienced by 94

Precise point positioning-real-time kinematic (PPP-RTK) technology can achieve accurate regional positioning with precise atmospheric corrections. However, traditional PPP-RTK algorithm research has focused on users in a reference station network. For out-of-network users, the extrapolated corrections typically contain significant errors, because it is difficult to model atmospheric delays within an area without sufficient reference stations. Poor-quality atmospheric corrections can significantly affect integer ambiguity resolution (IAR). We propose a reliable atmospheric correction generation method for out-of-network PPP-RTK users to maintain a continuous, ambiguity-fixed state in an inaccurately corrected observation environment. When users find that they have moved out of the reference station network, integer ambiguities acquired from the last epoch are used to estimate atmospheric corrections using an inversion calculation method. Due to the short epoch interval employed by PPP-RTK users, the estimated atmospheric corrections can properly correct delays in the observations of the current epoch. Before the users are forced to use the ionosphere-float model, they can employ the proposed method to continue utilizing the ionosphere-fixed model, maintaining the instantaneous ambiguity-fixed state for an extended period. This approach circumvents the need to directly adopt the ionosphere-float model, which introduces ionosphere parameters and necessitates time for them to converge. In experiments, both the integer ambiguity resolution success rate and correct rate demonstrated the superiority of the proposed method. In a kinematic PPP-ambiguity resolution (AR) experiment, using the estimated corrections, the mean success fix rates were within the range 31%-74%, and 93% of the horizontal errors were controlled to within 3 cm using the estimated corrections. This was significantly more favorable than the results obtained using traditional extrapolated corrections. It was confirmed that the proposed method possesses a much stronger ability to provide continuous ambiguity-fixed solutions and improve the accuracy of the coordinates for out-of-network users.

The Non-Terrestrial Network (NTN) is a wireless communications technology that enables direct communication between mobile terminals and satellites. It was developed by 3GPP in Release 17 and is based on new air interface technology. The aim of this technology is to offer widespread coverage, especially in areas where ground networks are challenging to establish. As 3GPP NTN architectures mature, further research and analysis is required to assess RF performance. This paper firstly presents an introduction to the development and evolution of NTN communication standards. Subsequently, analysis the challenges encountered by NTN terminals and testing. Then specifically discusses the requirements of 3GPP NTN terminal performance, with an emphasis on RF testing aspects. Finally, provides an outlook on 3GPP further work and study directions for NTN.

The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the Global Navigation Satellite System (GNSS) data collected from 12 stations situated in Yunnan, which are part of the Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) data from 2011 to 2022. The retrieved PWV data at Station KMIN were evaluated by the nearby radiosonde data, and the results show that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm, respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the stations’ heights and latitudes. Differences between average PWV in the wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. The amplitudes of the PWV annual cycles are 9.75–20.94 mm. The spatial variation of these amplitudes is similar to that of the average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed.

Low-Earth orbit (LEO) satellites have fast motion and thus provide rapid geometric change relation to the ground stations in a short period of time, which are expected to improve the positioning performance. In this study, a LEO constellation of 160 LEO satellites was simulated and simulated LEO observation data at global, European, and American stations were used to estimate the uncalibrated phase delays (UPDs) and achieve static and kinematic precise point positioning (PPP) ambiguity resolution (AR). The quality of the estimated UPD products were evaluated. More than 89% and 96% of the wide- and narrow-lane UPDs had a posteriori residual of less than 0.15 and 0.25 cycles, respectively. In static PPP, all ambiguity fixing rates were larger than 63%. Compared with float solutions, the average convergence time of the fix solutions at the global, European and American stations was accelerated by 8%, 6% and 7%, respectively; the average root-mean-square errors (RMSEs) in the east, north, and up components at the global, European and American stations were decreased by (22%, 20%, 15%), (36%, 34%, 28%) and (44%, 31%, 29%), respectively. In kinematic PPP, all ambiguity fixing rates were larger than 40%. Compared with float solutions, the average convergence time of the fix solutions at the global, European and American stations was accelerated by 4%, 19% and 14%, respectively; the average RMSEs in the east, north, and up components at the global, European and American stations were decreased by (10%, 10%, 7%), (27%, 22%, 11%) and (32%, 20%, 10%), respectively. In both static and kinematic PPP, AR accelerated the convergence time and improved the positioning accuracy.

Satellite-based positioning has gained popularity, but in bustling urban areas, signals face challenges like serious attenuation, affecting the reliability of positioning. To overcome this, we propose a seamless positioning approach that combines the BeiDou navigation satellite system (BDS) and 5G, specifically designed for complex urban settings. Initially, we establish static and dynamic positioning solutions using real 5G data measured through the time difference of arrival method, resulting in accurate 5G indoor positioning. Following this, we develop a combined BDS/5G positioning model incorporating joint switching based on satellite elevation angle and carrier-to-noise ratio (CNR). We conduct positioning experiments in intricate environments to validate the effectiveness of our model. The experimental outcomes demonstrate that our combined BDS/5G seamless positioning model, employing the joint switching strategy of satellite elevation angle and CNR, achieves impressive positioning accuracies ranging from decimeters to meters. This approach markedly enhances accuracy and reliability of seamless positioning in challenging urban scenarios.

This study proposes a model using 5G time-of-arrival data to assist global navigation satellite system precise point positioning ambiguity resolution. Specifically, the model addresses the problem of PPP requiring a long convergence time in partially satellite-occluded GNSS environments, such as urban canyons. First, we apply the ionosphere-free PPP model to estimate uncalibrated phase delays. Next, we combine real 5G data with GNSS data to determine whether introducing 5G observations will decrease the convergence time of the PPP solution. Experimental results reveal that the 5G-assisted PPP model can effectively improve the convergence efficiency of the float solution, lower the fixed time, and achieve greater positional reliability. Notably, the combination of GPS, BDS, and 5G with a sampling interval of 1 s obtains a fixed solution in an average of 1.12 min. Moreover, 5G-assisted GNSS positioning effectively compensates for partial satellite occlusion, optimizes the PDOP value, and speeds up ambiguity fixing. The introduction of three and more 5G base stations helps to obtain fixed solutions within 9 min when it is difficult to obtain fixed solutions relying only on GNSS. Our findings have important implications for improving the widespread applicability and effectiveness of satellite-based navigation systems in light of increasing urbanization and the rise of signal-occluding environments.

The ionosphere is a part of the Earth’s atmosphere with complex temporal and spatial distribution and variation. Radio occultation can obtain the vertical distribution of ionosphere, effectively making up for the deficiencies of ground-based Global Navigation Satellite System (GNSS) and other ionospheric sounding technologies; however, the inversion results are subject to the hypothesis model and observation accuracy, making it important to conduct quality assessment. The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) project started in 2006 and has been renewed since 2019. So far, a large amount of observation data has been accumulated. In this paper, we used the electron density profile data obtained by COSMIC-1 and COSMIC-2 to evaluate the quality. Four evaluation parameters, which only consider the profile’s self-characteristics, were used, and the spatiotemporal distribution characteristics of the data quality were analyzed. The experimental results show that the number of occultation electron density profiles of COSMIC-2 is significantly greater, which is about seven times greater than COSMIC-1. In terms of spatial distribution, the unqualified ratio of COSMIC-1 profiles is less than 25

The Swarm mission aims to study the principle and change regularities of the Earth’s magnetic field. Precise orbit determination is essential to the successful implementation of the mission and relevant scientific research. This article focuses on using two different double-difference methods to improve the accuracy of Swarm kinematic orbit determination. The accuracy of the kinematic orbit determination relies entirely on the space-borne observation data, independent of any dynamic parameters. The article analyzes the data quality of the Swarm space-borne global positioning system (GPS) receiver and presents a detailed introduction to the data pre-processing algorithms. The double-difference observation gathering and the applied orbit determination strategy using two different double-difference methods are discussed. The results of the kinematic orbits under different solar cycle conditions are presented, along with an evaluation based on analysis of GPS carrier phase residuals, subtracting from the post-processed orbits, and assessment with satellite laser ranging (SLR) measurements. The results show that the accuracy of the kinematic orbit determination is at the centimeter level for the three Swarm satellites’ orbit solutions. The daily root mean square (RMS) values of the three satellites’ phase residuals remain at around the 6 mm level. The RMS values of the position residuals between the kinematic orbits and the reduced dynamic orbits released by the European Space Agency (ESA) are at about the 2–3 cm level. The external evaluation with SLR measurements shows a good agreement with the ESA level, with the RMS values of the SLR residuals for kinematic orbits around 2 cm.