Real-time Kinematic Positioning Research Articles

Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique.

Sky-GVIO: Enhanced GNSS/INS/Vision Navigation with FCN-Based Sky Segmentation in Urban Canyon

Accurate, continuous, and reliable positioning is critical to achieving autonomous driving. However, in complex urban canyon environments, the vulnerability of stand-alone sensors and non-line-of-sight (NLOS) caused by high buildings, trees, and elevated structures seriously affect positioning results. To address these challenges, a sky-view image segmentation algorithm based on a fully convolutional network (FCN) is proposed for NLOS detection in global navigation satellite systems (GNSSs). Building upon this, a novel NLOS detection and mitigation algorithm (named S−NDM) uses a tightly coupled GNSS, inertial measurement units (IMUs), and a visual feature system called Sky−GVIO with the aim of achieving continuous and accurate positioning in urban canyon environments. Furthermore, the system combines single-point positioning (SPP) with real-time kinematic (RTK) methodologies to bolster its operational versatility and resilience. In urban canyon environments, the positioning performance of the S−NDM algorithm proposed in this paper is evaluated under different tightly coupled SPP−related and RTK−related models. The results exhibit that the Sky−GVIO system achieves meter-level accuracy under the SPP mode and sub-decimeter precision with RTK positioning, surpassing the performance of GNSS/INS/Vision frameworks devoid of S−NDM. Additionally, the sky-view image dataset, inclusive of training and evaluation subsets, has been made publicly accessible for scholarly exploration.

Decimeter-Level Accuracy for Smartphone Real-Time Kinematic Positioning Implementing a Robust Kalman Filter Approach and Inertial Navigation System Infusion in Complex Urban Environments.

New smartphones provide real-time access to GNSS pseudorange, Doppler, or carrier-phase measurement data at 1 Hz. Simultaneously, they can receive corrections broadcast by GNSS reference stations to perform real-time kinematic (RTK) positioning. This study aims at the real-time positioning capabilities of smartphones using raw GNSS measurements as a conventional method and proposes an improvement to the positioning through the integration of Inertial Navigation System (INS) measurements. A U-Blox GNSS receiver, model ZED-F9R, was used as a benchmark for comparison. We propose an enhanced ambiguity resolution algorithm that integrates the traditional LAMBDA method with an adaptive thresholding mechanism based on real-time quality metrics. The RTK/INS fusion method integrates RTK and INS measurements using an extended Kalman filter (EKF), where the state vector x includes the position, velocity, orientation, and their respective biases. The innovation here is the inclusion of a real-time weighting scheme that adjusts the contribution of the RTK and INS measurements based on their current estimated accuracy. Also, we use the tightly coupled (TC) RTK/INS fusion framework. By leveraging INS data, the system can maintain accurate positioning even when the GNSS data are unreliable, allowing for the detection and exclusion of abnormal GNSS measurements. However, in complex urban areas such as Qazvin City in Iran, the fusion method achieved positioning accuracies of approximately 0.380 m and 0.415 m for the Xiaomi Mi 8 and Samsung Galaxy S21 Ultra smartphones, respectively. The subsequent detailed analysis across different urban streets emphasized the significance of choosing the right positioning method based on the environmental conditions. In most cases, RTK positioning outperformed Single-Point Positioning (SPP), offering decimeter-level precision, while the fusion method bridged the gap between the two, showcasing improved stability accuracy. The comparative performance between the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 revealed minor differences, likely attributed to variations in the hardware design and software algorithms. The fusion method emerged as a valuable alternative when the RTK signals were unavailable or impractical. This demonstrates the potential of integrating RTK and INS measurements for enhanced real-time smartphone positioning, particularly in challenging urban environments.

Analysis of the gain factors of 5G-assisted BDS RTK positioning in urban environments

The joint utilization of the Fifth Generation Communications Technology (5G) and the Global Navigation Satellite System (GNSS) serves as a promising solution to address the challenges associated with insufficient visible satellites and lower observation quality in urban environments. 5G allows for the angle and distance measurements, augmenting the performance of Real-Time Kinematic (RTK) positioning. To quantify the improvement of 5G observations on RTK positioning, this paper proposes a float solution gain factor and the Ambiguity Dilution of Precision (ADOP) gain factor. Based on these gain factors, the theoretical analysis and simulation are performed. This study designs an extended Kalman filter for 5G-assisted BeiDou Navigation Satellite System (BDS) RTK positioning, employing both the Full Ambiguity Resolution (FAR) and Partial Ambiguity Resolution (PAR) modes. Our experiment verified the effectiveness of 5G-assisted BDS RTK positioning in mitigating outlier occurrences and improving the ambiguity fixing rate as well as the positioning accuracy. In the FAR and PAR modes, the Three-Dimensional (3D) spatial accuracy increased by 48% and 18.8%, respectively, and the results are consistent with theoretical analysis based on gain factors. The fixing rate of RTK increased from 11.11% to 13.93%, while it increased from 32.58% to 44.43% for the PAR mode. The assistance of 5G observations reduced the median error for the FAR mode from over 1.3m to 0.9 m, and the third quartile from 2.1m to 1.05 m. For the PAR mode, the median error decreased from 0.5m to 0.12 m, and the third and fourth quartiles decreased from 0.65m to 0.38 m.

Application of Atmospheric Augmentation for PPP-RTK with Instantaneous Ambiguity Resolution in Kinematic Vehicle Positioning

The long convergence time and non-robust positioning accuracy are the main factors limiting the application of precision single-point positioning (PPP) in kinematic vehicle navigation. Therefore, a dual/triple-frequency multi-constellation PPP-RTK method with atmospheric augmentation is proposed to achieve cm-level reliable kinematic positioning. The performance was assessed using a set of static station and kinematic vehicle positioning experiments conducted in Wuhan. In the static experiments, instantaneous convergence within 1 s and centimeter-level positioning accuracy were achieved for PPP-RTK using dual-frequency observation. For the kinematic experiments, instantaneous convergence was also achieved for dual-frequency PPP-RTK in open areas, with RMS of 2.6 cm, 2.6 cm, and 7.5 cm in the north, east, and up directions, respectively, with accuracy similar to short-baseline real-time kinematic positioning (RTK). Horizontal positioning errors of less than 0.1 m and 3D positional errors of less than 0.2 m were 99.54% and 98.46%, respectively. Additionally, after the outage of GNSS and during satellite reduction in obstructed environments, faster reconvergence and greater accuracy stability were realized compared with PPP without atmospheric enhancement. Triple-frequency PPP-RTK was able to further enhance the robustness and accuracy of positioning, with RMS of 2.2 cm, 2.0 cm, and 7.3 cm, respectively. In summary, a performance similar to RTK was achieved based on dual-frequency PPP-RTK, demonstrating that PPP-RTK has the potential for lane-level navigation.

An Effective Scheme for Modeling and Compensating Differential Age Errors in Real-Time Kinematic Positioning

In many real-time kinematic (RTK) positioning applications, reference observations are transmitted over wireless links that can experience frequent interruptions or substantial delays. This results in large differential ages between base and rover observations, which, in turn, leads to a deterioration in positioning performance. To bridge the significant age difference, in this work, we propose a simple and effective scheme for modeling and compensating for such errors. Firstly, the overall differential age error was modeled using truncated Taylor expansion. Then, a time-differenced carrier phase (TDCP)-based observation model was established to estimate the errors with the Kalman framework. Since estimating the receiver’s clock error is unnecessary, equivalent transformation and sequential filtering technology were adopted to significantly reduce the computational complexity. Furthermore, a predictor performance monitor was introduced to mitigate the integrity risks that may occur due to model mismatches. The effectiveness of this scheme was validated by static and dynamic field experiments. The static experiment results showed that when the differential age was 60 s, the GPS and BDS satellites’ overall root mean square error (RMSE) with the asynchronous RTK (ARTK) prediction method was 2.8 and 5.5 times that of the proposed method, respectively. Moreover, when the differential age was 120 s, these values were 3.3 and 5.4 times that of the proposed method, respectively. The field experiment results showed that when the differential age was 60 s, the integer ambiguity fixed rate and false fixed rate of the ARTK method were 0.90 and 1.63 times that of the proposed method, respectively. Finally, at a 120 s differential age, these values were 0.78 and 4.78 times that of the proposed, respectively.

An enhanced RANSAC-RTK algorithm in GNSS-challenged environments

ABSTRACT Outliers can significantly impact the accuracy and reliability of Global Navigation Satellite System (GNSS) real-time kinematic (RTK) positioning. To enhance the robustness of RTK in GNSS-challenged environments, we propose an enhanced random sample consensus RTK (RANSAC-RTK) algorithm capable of effectively handling multiple and continuous outliers. The enhancements to the RANSAC algorithm include threshold setting, pre-screening samples and sample verification tailored to the characteristics of GNSS data. The experimental results indicate that the standard RTK algorithm is vulnerable to outliers. By contrast, the enhanced RANSAC-RTK algorithm can effectively handle multiple and continuous outliers, resulting in 33% increase in the ambiguity fixing rate and 15% improvement in positioning accuracy.

Enhancing Tower Crane Safety: A UAV-Based Intelligent Inspection Approach

Tower cranes play a crucial role in the construction industry, facilitating the vertical and horizontal movement of materials and aiding in building construction, especially for high-rise structures. However, tower crane accidents can lead to severe consequences, highlighting the importance of effective safety management and inspection. This paper presents a novel approach to tower crane safety inspections using Unmanned Aerial Vehicles (UAVs) equipped with high-definition cameras and an intelligent inspection APP system. The system utilizes real-time kinematic (RTK) positioning and digital image processing to perform efficient and comprehensive inspections, reducing the reliance on manual labor and associated risks. A case study demonstrated the method’s practicality and effectiveness, with the UAV inspections capable of identifying 76.3% of major hazards, 64.8% of significant hazards, and 76.2% of general hazards within a 30-minute timeframe. Preliminary identification rates were also promising. Despite the initial requirement for manual drone piloting and the current manual review of images, the approach shows significant potential for enhancing safety in the construction industry. Future work will focus on integrating AI for hazard recognition and automating the inspection process further. The proposed method marks a step forward in tower crane safety management, offering a more efficient and accurate alternative to traditional inspection methods.

A novel mode of INS-aided BDS real-time high-rate and precise kinematic relative positioning between two moving platforms

ABSTRACT For kinematic relative positioning users between two moving platforms, limited communication bandwidth and computation ability usually cannot support real-time transmission of high-rate (≥10 Hz) BeiDou Navigation Satellite System (BDS) data. The performance of Ambiguity Resolution (AR) is also a major challenge in signal blocked and loss of lock environments. Based on BDS and Inertial Navigation System (INS) data, we develop a novel mode of INS-aided BDS kinematic relative positioning between two moving platforms, aiming to realize real-time, high-rate and precise positioning with low-cost communication modules in challenge environments. To achieve this goal, the INS-aided high-rate relative kinematic positioning and INS-aided BDS AR re-initialization methods are proposed. In this mode, the baseline bias caused by the different position datum is first defined and resolved. Then, high-rate INS data are assisted to obtain kinematic relative position when real-time kinematic positioning results are unavailable within 1 second. Once the BDS data are available, the predicted relative position is used as additional constraint to facilitate AR re-initialization and improve the kinematic positioning performance. The performance of these methods is discussed in a set of experiments with 1 Hz BDS data and 100 Hz INS data, and compared with the conventional method by sending raw BDS/INS measurements. The results show that the proposed methods can achieve an accuracy of about 5 cm for the INS-aided 100 Hz relative positioning in baseline components and lengths, which is equal to the conventional method, but the transmitted data have been sharply reduced by an average of nearly 80%. With the assistance of INS-predicted baseline constraint, the relative positioning performance has been further improved. The accuracy of baseline length errors is less than 4 cm, and the AR fixing rates keep larger than 95% during the experiment, while the wrongly fixed rates are reduced to less than 0.5%.

A Hopular based weighting scheme for improving kinematic GNSS positioning in deep urban canyon

Global navigation satellite system (GNSS) positioning performance in the urban dense environment experiences significant deterioration due to frequent non-line-of-sight (NLOS) and multipath errors. An accurate weighting scheme is critical for positioning, especially in urban environment. Traditional methods for determining the weights of observations typically rely on the carrier-to-noise density ratio (C/N0) and the elevations from satellites to receivers. Nevertheless, the performance of these methods is degraded in the dense urban settings, as C/N0 and elevation measurements fail to fully capture the intricacies of NLOS and multipath errors. In this paper, a novel GNSS observations weighting scheme based on Hopular GNSS signal classifier, which can accurately identify the LOS/NLOS signals using medium-sized training dataset, is proposed to improve the urban kinematic navigation solution in real-time kinematic positioning mode. Four GNSS features: C/N0, time-differenced code-minus-carrier, loss of lock indicator and satellite’s elevation, are employed in the training of the Hopular based signal classifier. The performance of the new method is validated using two urban kinematic datasets collected by a U-blox F9P receiver with a low-cost antenna, in downtown Calgary. For the first testing dataset, the results show that the Hopular based weighting scheme outperforms the three most commonly used GNSS observations weighting schemes: C/N0, elevation, and a combined C/N0-elevation approach. Approximately 10.089 m of horizontal root-mean-squared (RMS) positioning error and 12.592 m of vertical RMS error are achieved using the proposed method; with improvements of 78.83%, 46.82% and 43.27% on horizontal positioning accuracy and 54.00%, 47.51% and 49.69% on vertical positioning accuracy, compared to using C/N0, elevation and C/N0-elevation combined weighting schemes, respectively. For the second testing dataset, a similar performance is achieved with nearly 11.631 m of horizontal RMS error and 10.158 m of vertical RMS error; improvements of 64.58%, 32.90% and 22.40% on horizontal positioning accuracy and 71.99%, 65.24% and 55.88% on vertical positioning accuracy are achieved, compared to using C/N0, elevation and C/N0-elevation combined weighting schemes, respectively.

Assessment of compaction quality of highway subgrade via roller-integrated positive maximum velocity detection technique

ABSTRACT Quality control of compaction is vital for the long-term performance of highways. Specifically, the assessment of subgrade compaction quality is a key factor. Currently, compaction quality is primarily controlled by parameters set during compaction and subsequent random sampling tests post-construction. However, the impact of human factors and limitations of these sampling tests fall short of meeting the standards of large-scale precision construction. This study introduces a real-time monitoring index to characterise the compaction quality of silt subgrades: the maximum velocity compaction value (MVCV). This index is derived using a combination of the roller-integrated positive maximum velocity detection system and real-time kinematic Beidou positioning system (RTK-BDS). Furthermore, the Green spline interpolation method allows for estimating compaction quality values at any point. A case study from the Hengyong Highway project in China reveals a strong linear relationship between compaction parameters and compactness. This suggests the suitability of this method for characterising the compaction quality of such silt. This assessment approach facilitates a detailed evaluation of compaction quality across the entire construction zone. Hence, it aids in effectively minimising compaction defects, reducing unnecessary rework, and enhancing the overall compaction quality and efficiency of highway construction.

Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles

Real-time kinematic (RTK) positioning of the global navigation satellite systems (GNSS) is used to provide centimeter-level positioning accuracy. There are several ways to implement RTK but a Kalman filter-based RTK is preferred because of its superior capability to resolve GNSS carrier phase integer ambiguities. However, the positioning performance of the Kalman filter-based RTK is often compromised by various factors when it comes to determining a precise relative position vector between moving unmanned aerial vehicles (UAVs) equipped with low-cost GNSS receivers and antennas, where the locations of both GNSS antennas are not accurately known and change over time. Some of the critical factors that lead to a high rate of incorrect resolutions of carrier phase integer ambiguities are measurement time differences between GNSS receivers, frequent cycle slips with high noise in code and carrier phase measurements, and an improper Kalman filter gain due to a newly risen satellite. In this paper, effective methods to deal with those factors to achieve a seamless Kalman filter-based RTK performance in moving UAVs are presented. Using our extensive 45 flight tests data sets, conducted over a duration of 3 to 12 min, the RTK positioning results showed that the root-mean-square position error (RMSE) decreased by up to 95.13%, with an average of 65.31%, and that the percentage of epochs that passed the ratio test, which is the most common method for validating double differenced carrier phase integer ambiguity resolution, increased by up to 130%, with an average of 23.54%.

Studying the Repeatability of Measurements Obtained via Network Real-Time Kinematic Positioning at Different Times of the Day

The network real-time kinematic (NRTK) positioning technique is currently used in numerous applications. The aim of this study was to better understand the process of obtaining accurate positions by statistically evaluating the significance of differences between repeated measurements for a single point at different times of the day (morning, noon, and evening) using the Virtual Reference Station (VRS), Flächen Korrektur Parameter (FKP), and Master Auxiliary Concept (MAC) correction methods. An analysis of variance (ANOVA) was used to this effect. Further analysis was carried out to determine the accuracy and precision of the coordinate differences obtained via a static GNSS (global navigation satellite system) and by averaging the repeated measurements. It was determined that the accuracy and precision of the vertical component of the coordinates were lower than that of the horizontal component. The FKP correction method yielded the best results. It was observed that the accuracy and precision of the measurements taken at noon were the lowest. The ANOVA showed that the differences between repeated measurements were statistically significant and that there were outlier measurements. The results of this study are important for NRTK users to be able to statistically evaluate different measurement configurations and obtain positions with the desired accuracy and precision.

Research on Robust Adaptive RTK Positioning of Low-Cost Smart Terminals.

The performance of low-cost smart terminals is limited by the performance of their low-cost Global Navigation Satellite System (GNSS) hardware and chips, as well as by the impact of complex urban environments, which affect the positioning accuracy and stability of GNSS services. To this end, this paper proposes a robust adaptive Kalman filter for different environments that can be applied after data preprocessing. Based on the Kalman filter algorithm, a robust estimation approach is introduced into real-time kinematic (RTK) positioning to make judgments on the abnormal observation values of low-cost smart terminals, which amplifies the variance and covariance of the outlier observation equation, and reduces the impact of outliers on positioning performance. The Institute of Geodesy and Geophysics III (IGG III) function is used for regulation purposes, where prior information is modified and refreshed using the equivalent weight matrix and adaptive factors, thus reducing the impact of system model errors on system state estimation results. In addition, a robust factor is defined to adjust positioning deviation weighting between the pre- and post-test robust estimates. The experimental results show that after robust RTK positioning in the static experiments, the overall improvement in positioning accuracies of the Xiaomi 8, Huawei P40, Huawei mate40, and low-cost M8 receiver reached 29.6%, 31.3%, 32.1%, and 30.7%, respectively. Similarly, after applying the proposed robust method in the dynamic experiments, the overall positioning accuracies of the Xiaomi 8, Huawei P40, Huawei mate40, and the low-cost M8 receiver improved by 28.3%, 32.9%, 35.4%, and 26.2%, respectively. The experimental results reveal that an excellent positioning effect of a smartphone is positively correlated with robust RTK positioning performance. However, it is worth noting that when the positioning accuracy reaches a high level, such as the positioning results achieved using low-cost receivers, the robustness performance shows a relatively decreasing trend. This finding suggests that under the condition of high positioning accuracy, the sensitivity of specific positioning equipment to interference sources may increase, resulting in a decline in the effect of robust RTK positioning.

Resilient Ambiguity Resolution Strategy in GNSS Real-Time Kinematic Positioning for Urban Environments

Resilient Ambiguity Resolution Strategy in GNSS Real-Time Kinematic Positioning for Urban Environments

Analysis and Experimental Investigation of Steering Kinematics of Driven Steering Crawler Harvester Chassis

In the context of automatic driving, the analysis of the steering motion characteristics is critical for enhancing the efficiency of crawler harvesters. To address issues such as the low transmission efficiency and the large steering radius encountered by traditional crawler harvesters featuring hydrostatic drives, a driven steering crawler harvester chassis was designed. This involved analysis of the chassis transmission system structure and its steering characteristics under several conditions, including differential steering, differential direction reversal, and unilateral braking steering. The steering parameters were determined based on real-time kinematic positioning–global navigation satellite system (RTK-GNSS) measurements, and they were compared with theoretical predictions based on the crawler harvester steering kinematics. The slip rates and modified models of the crawler chassis for various steering modes were then obtained. The results indicated that the increase in the ratio between the running input and steering input speeds led to larger track steering radii and smaller average rotational angular velocities. Remarkably, the slopes of the linear fits of the tracked chassis steering parameters varied significantly under differential direction reversal and differential steering modes. Compared with the actual results, the correlation coefficient of the tracked chassis steering parameters fitting model is close to 1. The steering parameter model was deemed suitable for actual operational requirements. The results provide a valuable reference for designing navigation and steering models of crawler harvesters operating on different road surfaces.

BDS-3 RTK/UWB semi-tightly coupled integrated positioning system in harsh environments

Real-time kinematic (RTK) positioning is a commonly used technique in modern industry, which is limited by problems such as signal occlusion, attenuation, and multipath, especially in complex urban canyons. To maintain the consistency of centimeter-level accuracy, we adopt the ultra-wideband (UWB) enhanced BDS-3 RTK positioning algorithm. This paper proposed a semi-tightly coupled (STC) BDS-3 RTK/UWB integration positioning model. This model realizes the UWB and BDS-3 complement each other and integrate information in the position domain. Besides, height constraint is imposed on UWB positioning to mitigate the effect of poor positioning of UWB in height components. To verify the effectiveness of the above algorithm, we have compared and analyzed the positioning performance of the STC BDS-3 RTK/UWB integration model and single BDS-3 RTK model in different occlusion environments. The positioning performance of static and kinematic of BDS-3 RTK/UWB STC based on different number of UWB anchors is further analyzed. The real-world experiment results show that the positioning accuracy of the proposed method can reach centimeter-level. Moreover, the proposed model can obtain more accurate positioning results than those of using single system, and it shows more obvious advantages, especially in the occlusion environment. In the occlusion environment, the root means square error in the east, north, and up directions is improved from (0.629 m, 0.325 m, 1.160 m) of the BDS-3-only to (0.075 m, 0.074 m, 0.029 m). This study can provide a reference for the future development of high-precision, real-time, continuous positioning, navigation, and timing in complex urban environments.

Analysis of Multifrequency GNSS Signals and an Improved Single-Epoch RTK Method for Medium-Long Baseline

In recent years, the quantity of visible satellites has increased significantly due to multiple satellite systems that leaped forward. The BeiDou Navigation Satellite System (BDS) and Galileo satellite navigation system (Galileo) broadcast triple-frequency signals and above to users, thus enhancing the reliability, continuity, and availability of the single-epoch real-time kinematic (RTK) positioning. In this study, an improved single-epoch multifrequency multisystem RTK method is successfully developed for the medium-long baseline. First, the Galileo and BDS extra-wide-lane (EWL) ambiguities are fixed at a high success rate, and the Galileo and BDS wide-lane (WL) ambiguity is achieved via the transformation process. Second, the fixed WL ambiguities of Galileo and BDS are exploited to elevate the fixed rate of GPS WL ambiguity. Third, the parametric strategies for ionospheric delay are carried out to upregulate the narrow-lane (NL) ambiguity-fixed rate of GPS. Further, the real-time data are adopted for verifying the feasibility of the method developed in this study. The experimental results demonstrate the optimal carrier-to-noise density ratio (C/N0) of full operational capability (FOC) E5a/E5b at all frequencies, followed by IIR-M L1, and IIR-A/B L2 exhibits the worst performance. Generally, the multipath combination (MPC) of Galileo signals shows root mean square (RMS) values within 0.4 m, ordered as follows: E 1 > E 5 b > E 5 a . For the BDS-2, the B3 signal exhibits optimal performance, while the B1 signal is the worst. The RMS of MPC errors of L1 signals is smaller than the L2 signals for the GPS. Furthermore, under the 50 km baseline, the GPS NL ambiguity-fixed rate using the ionosphere-free (IF) combination reaches only 47.74% at the ratio threshold of 2. Finally, compared to the ionosphere-free combination method, the GPS NL ambiguity-fixed rate is increased by 45.52% with the presented method. The proposed approach broadens the future application of deformation monitoring in medium-long baseline scenarios.

Seasonal morphological evolution and migration of granule ripples in the Sanlongsha Dune Field, northern Kumtagh Sand Sea, China

Granule ripples are aeolian landforms that are armored against erosion by coarse grains at their crest. Their bimodal grain-size distributions and large dimensions distinguish them from normal wind ripples. The morphodynamics of granule ripples have attracted considerable attention because they provide a good example of coarse-grained aeolian bedforms. Previous studies have mainly focused on the morphological characteristics and formation process of granule ripples, but their evolution has been less studied. Using unpiloted aerial vehicle photogrammetry and real-time kinematic positioning, we conducted five field observations in the Sanlongsha Dune Field of northern China's Kumtagh Sand Sea. We analyzed the seasonal morphological evolution and migration of granule ripples, and the corresponding wind regimes. The morphometric parameters and migration rate showed distinctly different changes in different periods. The lowest height, the fastest migration, and most of the disintegration of granule ripples occurred in the season with the strongest wind. Although the proportions of wind speeds that exceeded the impact and fluid threshold velocities of coarse grains were low, these wind events significantly influenced the morphodynamics of granule ripples. Our findings and theoretical analysis demonstrate that the evolution of granule ripples is closely related to bimodal and unimodal transport events. To enhance our understanding of the morphodynamics of granule ripples, we must focus future research on the reptation and saltation of coarse grains.

Improving ambiguity resolution with common-antenna-based dual-board receiver for low-cost real-time kinematic positioning

Ambiguity resolution is of critical importance to the carrier phase-based real-time kinematic (RTK) positioning method. Improving the accuracy of float ambiguities is beneficial for achieving ambiguity resolution. However, the large measurement noise from low-cost receivers will worsen the estimation accuracy of float ambiguities, which affects the ambiguity resolution performance. In this contribution, to reduce the influence of large measurement noise on ambiguity resolution for low-cost receivers, an improved RTK method for ambiguity resolution is proposed to enhance the accuracy of float ambiguities by equipping the rover receiver with common-antenna-based dual global navigation satellite system (GNSS) boards instead of only one GNSS board. First, the dual-board design can increase the measurement redundancy of the same frequency to suppress the measurement noise. Second, because the common-antenna design can form a moving zero-baseline between the dual GNSS boards, the ambiguities between them can be easily fixed. Known fixed ambiguities can be used as constraints to strengthen the positioning model. Simulation and real-world static and kinematic experiments were conducted to test the proposed method. The results demonstrate that the proposed method can improve the accuracy of float ambiguities by increasing the redundancy of the measurements and introducing the constraints of the ambiguities, and the improved accuracy is about 20%. Compared with the traditional single-board RTK method, better ambiguity resolution performance can be achieved by taking advantage of the proposed common-antenna-based RTK method.