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The precision of deformation monitoring with Global Navigation Satellite System (GNSS) technology will degrade in complex environments, such as landslides. Furthermore, the loosely coupled (LC) model based on GNSS and accelerometer offers only limited improvement when dealing with continuous unreliable outliers. In this study, our work concentrates on the multisensor data process, and we introduce a tightly coupled (TC) algorithm that combines real-time kinematic (RTK) and accelerometer, in which the state and observation equations are described in detail. To address the issue of the accelerometer baseline shift (BS) potentially absorbing the errors from float coordinates when the fix ambiguities cannot be resolved, we further propose a virtual observation constraint model based on BS. This model is named as the novel tightly coupled (NTC) algorithm in this article, aiming to slow down the error accumulation in the acceleration integral process. Two experiments were conducted: a simulated deformation experiment and a real-world landslide monitoring task, to validate the effectiveness of the proposed algorithm. The TC algorithm notably enhances the fixed rate of integer ambiguities, achieving 74.37% and 99.97% fix rate in the two experiments, respectively, which represents a 25.37% and 51.67% improvement over the RTK solution. In the case of the NTC algorithm, it achieves more precise BS correction, which leads to a further positive effect on enhancing the fixed ambiguity resolution in the simulated experiment. In the real-world landslide experiment, while the improvement of the fix rate is nonsignificant, however, the ratio value results still demonstrate a noticeable contribution.

Current vehicle Lidar-inertial odometry (LIO) is prone to error accumulation, particularly when low-cost equipment is utilized in large-scale scenes without loop closure. In this article, we propose a novel approach to address this problem by introducing the ground nonholonomic constraint (NHC) and enhancing LIO with the NHC factor, referred to as NHC-LIO. The conventional NHC model assumes zero values for the lateral and vertical velocities, which proves to be challenging to adapt to complex and rapidly changing practical applications. Thus, we further design a convolutional neural network that applies a nine-axis inertial measurement unit (IMU) to predict reliable NHC pseudo-observation in real-time. During the factor graph optimization stage, the NHC noises and the corresponding information matrix are calculated adaptively based on the prior vehicle motion state, further enhancing the robustness of the LIO assisted with the NHC factor. Extensive evaluation and verification of the NHC-LIO using the KITTI dataset demonstrates its state-of-the-art performance. The NHC factor effectively mitigates LIO drift, especially in the vertical direction when loop closure detection is not available.

Expansive soils pose a significant challenge in geotechnical engineering, especially in coastal areas. While research has mainly focused on their elastic properties, this study explores the overlooked aspect of inelastic subsidence during prolonged droughts, utilizing decade‐long GPS datasets from the University of Houston Coastal Center. Our findings reveal substantial subsidence, approximately one to two dm, during the summer droughts of 2018, 2020, 2022, and 2023, due to compaction within the upper 4 m of expansive soils. Inelastic subsidence constitutes roughly 10% of the total subsidence, resulting in step‐like permanent land elevation loss over time. Notably, drought‐induced subsidence is prominent in open‐field areas with expansive soils but is minor in built‐up areas or in non‐expansive soil regions. The occurrence of inelastic subsidence challenges traditional assessments of relative sea‐level rise and coastal flooding, emphasizing the need to consider it in coastal infrastructure planning for enhanced resilience against climate uncertainties. Expansive soils, often found in coastal regions, are known for causing issues like land shifts and unstable buildings. Our research adds a new dimension: prolonged droughts can lead to significant, irreversible sinking of expansive soils, permanently lowering the elevation of open land areas. Using a decade of GPS data, we found that during droughts, these soils can sink considerably and will not fully recover. Interestingly, in developed regions where pavement covers the soils, thereby minimizing moisture loss, this sinking is observed to be minimal. In the Galveston coastal area, drought‐induced sinking can reach one to two dm, with irreversible subsidence making up about 10% of the total subsidence. This becomes a growing concern as droughts become more frequent due to climate change, especially in coastal areas. Additionally, our results suggest that current methods for estimating sea‐level rise may be missing a key factor: we have underestimated the speed at which uninhabited coastal lands are sinking because we did not account for this irreversible sinking due to droughts. Prolonged droughts cause up 2 dm of subsidence in shallow expansive soils (<4 m deep) in Galveston, TX, 10% of which is inelasticProlonged droughts lead to notable subsidence in open fields with expansive soils, but less in built‐up areas or non‐expansive soil areasRecurring droughts lead to permanent land elevation losses in open‐field coast, possibly skewing sea‐level rise and flood risk projections Prolonged droughts cause up 2 dm of subsidence in shallow expansive soils (<4 m deep) in Galveston, TX, 10% of which is inelastic Prolonged droughts lead to notable subsidence in open fields with expansive soils, but less in built‐up areas or non‐expansive soil areas Recurring droughts lead to permanent land elevation losses in open‐field coast, possibly skewing sea‐level rise and flood risk projections

The atomic clock is the core of the Global Navigation Satellite System (GNSS), which plays an important role in Positioning, Navigation and Timing (PNT) services. However, the long-term characteristics of the global BeiDou navigation system (BeiDou-3) and comparison of satellite clocks performance for the five GNSSs have not yet been revealed. We evaluated and analysed the performance of atomic clocks for BeiDou-3 and other GNSSs using satellite clock offset data. The important contributions are summarised as follows. (1) The phase and frequency sequences of Passive Hydrogen Maser (PHM) and rubidium (Rb) atomic clocks for BeiDou-3 are more stable and continuous than those of the regional BeiDou navigation system (BeiDou-2). The clock offset model precision and average frequency stability at 10,000, 1000, and 100 s for the BeiDou-3 satellite clock offsets are 0.15 ns and 6.8 × 10–14, respectively, exhibiting significant improvements compared with those for BeiDou-2. (2) The performance of cesium (Cs) atomic clocks installed on Global Positioning System (GPS) Block IIF are more inferior than Global Navigation Satellite System (GLONASS), which may be due to the poor level of Cs atomic clock manufacturing technology or obvious noise characteristics. Thus, more attention should be paid to these two satellite clocks. (3) A comparison of various indicators revealed that the satellite clocks for Galileo are the best, followed by BeiDou-3, GPS Block IIF satellites with Rb atomic clocks, Quasi-Zenith Satellite System (QZSS), BeiDou-2, GLONASS, GPS Block IIR, and Block IIR-M. The GPS Block IIF satellites equipped with a Cs atomic clock shows the poorest performance.

The service areas of the BeiDou Navigation Satellite System (BDS) are China, the Asia–Pacific, and the world via three stages of development. The BDS constellation is designed to maintain a geostationary Earth orbit (GEO), inclined geosynchronous orbit (IGSO), and medium Earth orbit (MEO). These orbital maneuvers yield certain difficulties for data processing, especially for the BeiDou satellites, such as a decrease in the real-time service performance, which causes a missing precise orbit product from the maneuvered satellites. The frequencies of the orbital maneuvers for the GEO and IGSO satellites are higher than those for the MEO satellites. The maneuvering time and strategy cannot be obtained by common users due to secrecy, which can yield a decline in the service performance of the BDS. Thus, in this study, we analyzed the variation in the orbital semimajor axis for the satellites. The long-term variation in the orbital semimajor axis changes linearly. The short-term variation in the orbital semimajor axis also has periodicity, which can be described by the sum of sine functions. According to the long-term variation in the orbital semimajor axis for the satellites, we propose a detection method for in-plane orbital maneuvering of the GEO, IGSO, and MEO satellites. We then propose a detection method for out-of-plane maneuvering of the GEO satellites. BDS and Global Positioning System (GPS) data from the broadcast ephemeris were analyzed to verify the proposed methods. The experimental results from 2013 to 2019 show that the frequency of orbital maneuvering is approximately once a month for the GEO satellites, once every six months for the IGSO satellites, and once every 1.4 years for the MEO satellites.

AbstractThe safety of the Three Gorges Reservoir, the world’s largest hydraulic project, has attracted significant attention. Among the landslides in the area, the Xinpu landslide complex stands out as one of the largest. However, accurately detecting and interpreting the timing and magnitude of its movement remains a challenge. This study introduces a framework using interferometric synthetic aperture radar (InSAR) for investigating the Xinpu landslide complex. In areas with dense vegetation like Xinpu, estimating landslide movement has traditionally been problematic. The proposed method, based on independent component analysis-assisted intermittent multi-temporal InSAR, overcomes this limitation by retrieving complete and reliable time-series deformation without prior deformation models. Through an analysis of Sentinel-1 datasets covering a period of 4 years, our approach significantly enhances deformation density and accuracy in comparison to other multi-temporal InSAR techniques. When contrasted with these methods, our proposed approach exhibits an average RMS improvement of up to 46.2% between GNSS measurements and the InSAR-derived results. The complete time-series deformation reveals diverse triggers at different sections of the Xinpu landslide complex. Precipitation, with a 45-day lag, primarily controls the landslide, while the toe of the landslide shows joint effects from precipitation and water level fluctuations, with a 14-day lag. Cross-correlation analysis estimates lag times for deformation responses to different triggers and enables discussion of the complex’s kinematic process. Overall, this approach enhances InSAR performance in vegetated areas and aids in uncovering landslide triggers for effective hazard mitigation.

Currently, Global Navigation Satellite System (GNSS) Real-Time Kinematic positioning (RTK) and Precise Point Positioning (PPP) techniques are widely employed for real-time monitoring of landslides. However, both RTK and PPP monitoring techniques have their limitations, such as limited service coverage or long convergence times. PPP-RTK technique which integrates RTK and PPP is a novel approach for monitoring landslides with the advantages of rapid convergence, high-precision, and a wide service area. This study summarizes the limitations of RTK, PPP, and PPP-RTK monitoring techniques and suggests some improved strategies. Their performances are compared and analyzed using real monitoring data. The experiment results demonstrate that RTK is the best option for small-scale (the baseline distance < 15 km) and real-time landslide monitoring without considering the cost. PPP technique converges to centimeter-level accuracy in tens of minutes, only suitable for the stability analysis of reference stations. Over a large area (the baseline distance < 100 km), PPP-RTK can provide excellent horizontal accuracy and adapt the service range in response to the demand for monitoring accuracy, as the vertical accuracy is significantly impacted by the service range and elevation difference. Finally, the characteristics of three techniques are integrated to form a comprehensive landslide monitoring technique that considers intelligence, robustness, and real-time.

The Real-Time Kinematic (RTK) positioning method of the Global Navigation Satellite System (GNSS) has been widely used for landslide monitoring. The stability of its reference station is crucial to obtain accurate and reliable monitoring results. Unstable reference stations due to the geological environment and human activities are difficult to detect and in practical applications often ignored. As a result, it affects the positioning solutions and subsequently the interpretation and detection of landslide motions, which must be addressed in GNSS landslide monitoring. To solve this problem, we propose using the Precise Point Positioning (PPP) technique to analyze the stability of the reference station by verifying its position. The deformations of the monitoring stations are then compensated. First, the reference station coordinates are obtained by the PPP technique and tectonic motion is considered in data processing. The change or breakout of the reference station position is then determined using a cumulative sum control chart method. Finally, each monitoring station’s displacements are compensated according to the displacements of the reference station. According to the results of the Tengqing landslide experiment, the PPP technique can be used in GNSS landslide monitoring to analyze the stability of reference stations. With PPP, millimeter-level accuracy for the coordinates of reference stations is achieved. Compared to the traditional deformation series, the compensated displacement series more reliably reflects the landslide motions. This study will increase the reliability of monitoring results and contribute to implementing GNSS in monitoring landslides.

Currently, resolution of the BeiDou2 satellites differential code bias (DCB) is greatly limited by the number of tracking stations and data quality. To improve the precision of calculated DCBs, an adaptive control method, based on elevation angle weight and posteriori variance factor, is proposed. The feasibility and precision of the proposed method are verified via analysis of the DCBs precision and application of single‐frequency precise point positioning (SF‐PPP). The results show that the long‐term variation of the daily BeiDou2 satellite DCB sequence is stable and the precision of DCBs of BeiDou2 satellites in different constellations is different. In addition, it is found by using periodogram and fast Fourier transform (FFT) methods that weekly and daily periodic signals exist in the daily BeiDou2 satellite DCB products. The results of SF‐PPP show that root mean square (RMS) horizontal residual is reduced by 10% and convergence time is shortened significantly using DCB products. © 2018 Institute of Navigation