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The Indian Regional Navigation Satellite System (IRNSS) is an elegant system for positioning of users in India. It also provides its services to an extended region. In the IRNSS, to find the unknown user location, the used pseudo range method gives only meter-level accuracy. In order to reduce the noise impacts on measurement, and to improve position accuracy (meter-level to centimeter-level) for IRNSS, in this paper, carrier phase (CP) measurement technique is employed. Here, CP-based differential techniques are proposed to find the unknown user/rover receiver position. The corresponding incremental position (positional error) is computed in both double difference (DD) and triple difference (TD) techniques (with and without filter). In this paper, the CP measurement-based differencing techniques (DD/TD) supported with recursive filter (Kalman) is analyzed. This type of methodology is mostly useful in surveying applications. With this approach, the errors are reduced greatly, and the obtained accuracy is in centimeters. The filter utilizes and projects the previous and current activity data to estimate the float response. In this work, an iterative algorithm is used to find the precise position of the rover. For a short baseline, the obtained positional errors are compared for DD and TD with/without recursive filter. To simulate the real-time data, MATLAB programming is used. The obtained root mean square error (RMSE) in position estimation in IRNSS is 2.3391 m and 0.6901 m with DD, and 0.1079 m and 0.0518 m with TD without and with filter respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Tiangong University is the property of Journal of Tianjin Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In an environment where multiple nodes move without fixed points, such as in swarms of unmanned robots, understanding the relative positions of the robots is crucial. This paper proposes a new relative positioning technology for understanding the relative positions of nodes in environments where GPS usage is difficult, such as indoors. Specifically, the proposed technology is a new deep neural network (DNN) technique that performs relative positioning using distance information between nodes. This paper proposes two new methods to enhance the performance of relative positioning based on existing DNN techniques. The first method ensures a minimum distance between reference nodes, and the second method involves selecting the optimal reference node. Through computer simulations, it was confirmed that coordinate estimation performance improves when a minimum distance between reference nodes is maintained. Based on these results, the method for selecting the optimal reference node was developed to choose nodes with greater distances between them. Using this method increases the accuracy of coordinate estimation compared to existing methods. [ABSTRACT FROM AUTHOR]

In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, and poor communication link quality, distance errors and even missing distance values between some nodes are inevitable. To address these issues, this paper proposes a low-rank optimization algorithm based on the eigenvalue scaling of the distance matrix. By gradually limiting the eigenvalues of the observed distance matrix, the algorithm reduces the rank of the matrix, bringing the observed distance matrix closer to the true value without errors or missing data. This process filters out distance errors, estimates and completes missing distance elements, and ensures high-precision calculations for subsequent topology perception and relative positioning. Simulation experiments demonstrate that the algorithm exhibits significant error filtering and missing element completion capabilities. Using the F-norm metric to measure the relative deviation from the true value, the algorithm can optimize the relative deviation of the observed distance matrix from 11.18% to 0.25%. Simultaneously, it reduces the relative positioning error from 518.05 m to 35.24 m, achieving robust topology perception and relative positioning for the drone swarm formation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Monitoring horizontal displacements, such as landslides and tectonic movements, holds great importance and high-cost geodetic GNSS equipment stands as a crucial tool for the precise determination of these displacements. As the utilization of low-cost GNSS systems continues to rise, there is a burgeoning interest in evaluating their efficacy in measuring such displacements. This evaluation is particularly vital as it explores the potential of these systems as alternatives to high-cost geodetic GNSS systems in similar applications, thereby contributing to their widespread adoption. In this study, we delve into the assessment of the potential of the dual-frequency U-Blox Zed-F9P GNSS system in conjunction with a calibrated survey antenna (AS-ANT2BCAL) for determining horizontal displacements. To simulate real-world scenarios, the Zeiss BRT 006 basis-reduktionstachymeter was employed as a simulation device, enabling the creation of horizontal displacements across nine different magnitudes, ranging from 2 mm to 50 mm in increments of 2, 4, 6, 8, 10, 20, 30, 40, and 50 mm. The accuracies of these simulated displacements were tested through low-cost GNSS observations conducted over a 24 h period in open-sky conditions. Additionally, variations in observation intervals, including 3, 6, 8, and 12 h intervals, were investigated, alongside the utilization of the relative positioning method. Throughout the testing phase, GNSS data were processed using the GAMIT/GLOBK GNSS (v10.7) software, renowned for its accuracy and reliability in geodetic applications. The insightful findings gleaned from these extensive tests shed light on the system's capabilities, revealing crucial information regarding its minimum detectable displacements. Specifically, the results indicate that the minimum detectable displacements with the 3-sigma rule stand at 22.8 mm, 11.7 mm, 8.7 mm, and 4.8 mm for 3 h, 6 h, 8 h, and 12 h GNSS observations, respectively. Such findings are instrumental in comprehending the system's performance under varying conditions, thereby informing decision-making processes and facilitating the adoption of suitable GNSS solutions for horizontal displacement monitoring tasks. [ABSTRACT FROM AUTHOR]

Android smartphone has gained attention in precise positioning applications since it can collect raw observable GNSS (Global Navigation Satellite System) data. Some studies have reported that the positioning accuracy may reach the sub-decimeter level. However, these studies mostly rely on a flagship Android smartphone that is made with better internal hardware, while the use of a non-flagship Android smartphone is not reported for this field. In this study, therefore, we explore non-flagship Android smartphones for positioning applications. We assessed the observable data quality and positioning performance of two non-flagship Android GNSS smartphones of a Samsung M21 and a Redmi Note 7. The data quality assessment includes satellite tracking and carrier-to-noise density ratio analysis. Also, the positioning performance was assessed for Single Point Positioning (SPP) and relative positioning methods in static and open-sky conditions. In addition, the residual properties of GNSS measurements were also evaluated. The results were further compared to the high-grade GNSS device. We found that the observable pseudorange and carrier phase measurements from Android smartphones were about 70 % and 36 % of what high-grade GNSS obtained. Furthermore, within a span of 1 h of observations, a considerable amount of cycle slips, amounting to as many as 518 instances, were noted in the observations from Android GNSS devices. While for the carrier-to-noise density ratio in Android smartphones, it was estimated to be about 15 dB-Hz lower than in high-grade GNSS devices. The spread of the residuals for pseudorange and carrier phase from Android smartphones was estimated to be about ±15 and ±6 m, respectively. The 3D positioning error for SPP was estimated to be about 4.7 m, with a position spread reaching tens of meters. At the same time, the 3D positioning error was calculated to be 4.6 m with the estimated standard error at the centimeter level when using the relative positioning method. To improve the positioning performance, applying a C/N0 mask to the observations become the best solution. The 3D positioning error for the relative positioning method reduces to 2.7 m when applying a C/N0 mask of 30 dB-Hz. The observable data quality of non-flagship Android GNSS devices possibly causes relatively poor performance of positioning applications. [ABSTRACT FROM AUTHOR]

In recent years, smartphones have emerged as the primary terminal for navigation and location services among mass users, owing to their universality, portability, and affordability. However, the highly integrated antenna design within smartphones inevitably introduces interference from internal signal sources, leading to a misalignment between the antenna phase center (APC) and the antenna geometric center. Accurately determining a smartphone's APC can mitigate system errors and enhance positioning accuracy, thereby meeting the increasing demand for precise and reliable user positioning. This paper delves into a detailed analysis of the generation of Global Navigation Satellite System (GNSS) receiver antenna phase center errors and proposes a method for correcting the receiver antenna phase center. Subsequently, a smartphone positioning experiment was conducted by placing the smartphone on an observation column with known coordinates. The collected observations were processed in static relative positioning mode, referencing observations from geodetic-grade equipment, and the accuracy of the static relative positioning fixed solution was evaluated. Following weighted estimation, we determined the antenna phase center of the Xiaomi Mi8 and corrected the APC. A comparison of the positioning results of the Xiaomi Mi8 before and after APC correction revealed minimal impact on the standard deviations (STDs) but significant influence on the root mean square errors (RMSEs). Specifically, the RMSEs in the E/N/U direction were reduced by 59.6%, 58.5%, and 42.0%, respectively, after APC correction compared to before correction. Furthermore, the integer ambiguity fixing rate slightly improved after the APC correction. In conclusion, the determination of a smartphone's APC can effectively reduce system errors in the plane direction of GNSS positioning, thereby enhancing smartphone positioning accuracy. This research holds significant value for advancing high-precision positioning studies related to smartphones. [ABSTRACT FROM AUTHOR]

Autonomous agricultural vehicles rely on accurate real-time positioning information to perform precise farming operations, including seeding, fertilizing and harvesting. Although many positioning solutions exist on the market, their cost is out of many farmers' budgets. Relative positioning can be used for accurate positioning purposes in agriculture applications. Despite the fact that several research attempts have addressed the issue of relative positioning, no agriculture-specific study appears to exist in the literature at the time of writing. In this paper, the opportunity of using real-time GPS-based relative positioning for agriculture applications instead of the typical absolute positioning solutions has been investigated. To this end, we propose to implement and evaluate different relative positioning approaches, namely absolute position differencing, pseudorange single differencing as well as pseudorange double differencing (PDD). Real-world experiments, including static and dynamic scenarios, have been conducted using low-cost single-frequency GPS receivers in an open sky environment and an agricultural field. The obtained results show the superiority of the PDD approach with about 76% of estimation errors less than 1 m in the open sky environment and more than 97% of estimation errors less than 3 m in the agricultural field, representing a considerable accuracy enhancement compared to GPS standalone positioning. [ABSTRACT FROM AUTHOR]

A priori variance–covariance matrix (VCM) estimation of global navigation satellite systems (GNSS) double difference observations in relative positioning is challenging. Existing methods have been limited to estimate variances only or present variables challenging to acquire a priori, unfeasible for observation planning. Ignoring the covariances produces misleading results and compromises reliance on GNSS positioning design. In this study, we propose models to estimate the VCM a priori for planning, based on simple variables accessible to any professional: observation time span, vector length, and ephemeris type. Using a database of over 140,000 GNSS vectors with double difference (DD) observations, we group the data by time span and length range and extract standard deviations and covariances for the linear regression process. The Isolation Forest algorithm is employed to filter outlying observations. Our models provide standard deviations and root square covariances in a local coordinate system, requiring only vector length, observation time span, and ephemeris type as input. Additionally, the equations can be easily implemented in a simple spreadsheet. The results show high coefficients of determination (R2 > 0.8). We tested the models in a simulated GNSS network and verified broadcast ephemeris resulted in 6.5 to 16.7 times larger error ellipsoids compared to the precise ephemeris, indicating higher uncertainty. Ellipsoids differed in flattening and orientation when compared to the null covariance (variance only) approach. Although VCM models better reflect the precision of relative positioning observations, they did not affect the number of non-controllable observations in the observation's reliability tests. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, and poor communication link quality, distance errors and even missing distance values between some nodes are inevitable. To address these issues, this paper proposes a low-rank optimization algorithm based on the eigenvalue scaling of the distance matrix. By gradually limiting the eigenvalues of the observed distance matrix, the algorithm reduces the rank of the matrix, bringing the observed distance matrix closer to the true value without errors or missing data. This process filters out distance errors, estimates and completes missing distance elements, and ensures high-precision calculations for subsequent topology perception and relative positioning. Simulation experiments demonstrate that the algorithm exhibits significant error filtering and missing element completion capabilities. Using the F-norm metric to measure the relative deviation from the true value, the algorithm can optimize the relative deviation of the observed distance matrix from 11.18% to 0.25%. Simultaneously, it reduces the relative positioning error from 518.05 m to 35.24 m, achieving robust topology perception and relative positioning for the drone swarm formation.

The paper is devoted to solving the problem of determining the relative spatial arrangement and orientation of objects. The task was set: to show the fundamental possibility of spatial and angular relative positioning when using the parameters of the magnetic field gradient in tensor form and in the form of gradient of an absolute value vector as measurement information for the magnetic field of a local dipole source. The solution of the problem is presented along with the features and limitations for both forms of representation are considered. The principles of construction of magnetic gradiometry measurement systems are briefly described, the limitations of technical implementation are considered, and the benefits of using an alternating magnetic field source is outlined. The results of modelling are presented, proving the possibility of using the proposed positioning method for various engineering problems. [ABSTRACT FROM AUTHOR]

Advanced driving assistance system (ADAS) in autonomous driving vehicles usually obtains local relative positioning information only by identifying lane lines, and how to avoid collisions when there are jumps in lane lines and encountering obstacles is a key technology that ADAS needs to solve urgently. Therefore, this paper proposes a local dynamic trajectory planning method based on relative positioning. First, the grid sampling is performed in the vehicle curve coordinate system, and the candidate path set in the vehicle coordinate system is obtained after coordinate transformation. Then, the path set is filtered through the collision check and evaluation function to obtain the optimal path without collision. Secondly, the maximum planning speed is adaptively adjusted according to the curvature and lateral deviation of the optimal path, and the speed curve is obtained by the improved trapezoidal speed planning method. Finally, the speed curve is smoothed by the gradient descent algorithm to avoid sudden acceleration. The simulation and real vehicle test results show that the algorithm adopted in this paper can stably correct deviation, avoid obstacles and follow the car in the local relative positioning scene with curves. In the scenario without obstacles, the maximum lateral deviation of the algorithm in the simulation and real vehicle experiment is reduced by 36.7% and 28.6% respectively, the average lateral acceleration is reduced by 29.6% and 46.4% respectively, and the travelling comfort and vehicle safety are high.

In the present work, an artificial neural network (ANN) will be developed to estimate the relative rotation and translation of the autonomous mobile robot (AMR). The ANN will work as an iterative closed point, which is commonly used with the singular value decomposition algorithm. This development will provide better resolution for a relative positioning technique that is essential for the AMR localization. The ANN requires a specific architecture, although in the current work a neural architecture search will be adapted to select the best ANN for estimating the relative motion. At the end, these ANNs will be compared with conventional algorithms to check the good performance of adopting an intelligent method for relative positioning estimation. [ABSTRACT FROM AUTHOR]

Indian mapping agency, Survey of India (SOI), is ambitiously implementing nation-wide network of Continuously Operating Reference Stations (CORS) to improve the accuracy of derived three-dimensional relative positions of the Global Navigational Satellite System (GNSS) over the regions. However, it does not only improve the accuracy but also reduce the cost of reliable data acquisition. Recently, SOI has established a number of CORS networks and continues to cover the entire India for its suitability in the infrastructure projects and in the generation and updating of revenue maps. The present article attempts to measure the accuracy of a single baseline static survey carried out using the CORS network by studying fifteen stations established in different parts of Uttar Pradesh and Madhya Pradesh. To study the accuracy using the established CORS network, 7 days of continuous observed data from fifteen stations of Uttar Pradesh and Madhya Pradesh have been processed. The study has been done taking a base–rover combination with varying distance (D) from 30 to 160 km and the time duration (T) at 20 min, 1 h, 2 h, 4 h, 8 h, 12 h, and 24 h using observational CORS data (including both GPS and GLONASS). The present study shows that (i) dependence of accuracy on distance (D) up to 160 km is almost negligible when final ephemeris data of GPS satellite orbit (International GPS Service) have been used and (ii) the positional accuracy improved with an increase in observation duration. Finally, regression analysis has been done to establish the relationship between distance (D), duration (T), and accuracy for the given CORS network. [ABSTRACT FROM AUTHOR]

Monitoring horizontal displacements, such as landslides and tectonic movements, holds great importance and high-cost geodetic GNSS equipment stands as a crucial tool for the precise determination of these displacements. As the utilization of low-cost GNSS systems continues to rise, there is a burgeoning interest in evaluating their efficacy in measuring such displacements. This evaluation is particularly vital as it explores the potential of these systems as alternatives to high-cost geodetic GNSS systems in similar applications, thereby contributing to their widespread adoption. In this study, we delve into the assessment of the potential of the dual-frequency U-Blox Zed-F9P GNSS system in conjunction with a calibrated survey antenna (AS-ANT2BCAL) for determining horizontal displacements. To simulate real-world scenarios, the Zeiss BRT 006 basis-reduktionstachymeter was employed as a simulation device, enabling the creation of horizontal displacements across nine different magnitudes, ranging from 2 mm to 50 mm in increments of 2, 4, 6, 8, 10, 20, 30, 40, and 50 mm. The accuracies of these simulated displacements were tested through low-cost GNSS observations conducted over a 24 h period in open-sky conditions. Additionally, variations in observation intervals, including 3, 6, 8, and 12 h intervals, were investigated, alongside the utilization of the relative positioning method. Throughout the testing phase, GNSS data were processed using the GAMIT/GLOBK GNSS (v10.7) software, renowned for its accuracy and reliability in geodetic applications. The insightful findings gleaned from these extensive tests shed light on the system’s capabilities, revealing crucial information regarding its minimum detectable displacements. Specifically, the results indicate that the minimum detectable displacements with the 3-sigma rule stand at 22.8 mm, 11.7 mm, 8.7 mm, and 4.8 mm for 3 h, 6 h, 8 h, and 12 h GNSS observations, respectively. Such findings are instrumental in comprehending the system’s performance under varying conditions, thereby informing decision-making processes and facilitating the adoption of suitable GNSS solutions for horizontal displacement monitoring tasks.

Global Navigation Satellite Systems (GNSS) with weak anti-jamming capability are vulnerable to intentional or unintentional interference, resulting in difficulty providing continuous, reliable, and accurate positioning information in complex environments. Especially in GNSS-denied environments, relying solely on the onboard Inertial Measurement Unit (IMU) of the Micro Aerial Vehicles (MAVs) for positioning is not practical. In this paper, we propose a novel cooperative relative positioning method for MAVs in GNSS-denied scenarios. Specifically, the system model framework is first constructed, and then the Extended Kalman Filter (EKF) algorithm, which is introduced for its ability to handle nonlinear systems, is employed to fuse inter-vehicle ranging and onboard IMU information, achieving joint position estimation of the MAVs. The proposed method mainly addresses the problem of error accumulation in the IMU and exhibits high accuracy and robustness. Additionally, the method is capable of achieving relative positioning without requiring an accurate reference anchor. The system observability conditions are theoretically derived, which means the system positioning accuracy can be guaranteed when the system satisfies the observability conditions. The results further demonstrate the validity of the system observability conditions and investigate the impact of varying ranging errors on the positioning accuracy and stability. The proposed method achieves a positioning accuracy of approximately 0.55 m, which is about 3.89 times higher than that of an existing positioning method. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Compared to traditional vehicles, the underwater bionic manta ray vehicle (UBMRV) is highly maneuverable, has strong concealment, and is an emerging research field in underwater vehicles. Based on the completion of the single-body research, it is crucial to research the swarm of UBMRVs for the implementation of complex tasks, such as large-scale underwater detection. The relative positioning capability of the UBMRV is the key to realizing a swarm, especially when underwater acoustic communications are delayed. To solve the real-time relative positioning problem between individuals in the UBMRV swarm, this study proposes a relative positioning method based on the combination of the improved object detection algorithm and binocular distance measurement. To increase the precision of underwater object detection in small samples, this paper improves the original YOLOx algorithm. It increases the network's interest in the object area by adding an attention mechanism module to the network model, thereby improving its detection accuracy. Further, the output of the object detection result is used as the input of the binocular distance measurement module. We use the ORB algorithm to extract and match features in the object-bounding box and obtain the disparity of the features. The relative distance and bearing information of the target are output and shown on the image. We conducted pool experiments to verify the proposed algorithm on the UBMRV platform, proved the method's feasibility, and analyzed the results. [ABSTRACT FROM AUTHOR]

In the Global Navigation Satellite System (GNSS), array antennas, widely used in anti-jamming applications but seldom used in high-precision applications, can obstruct successful ambiguity resolution (AR) by introducing large phase biases. However, the phase pattern of the entire array antenna can potentially be large and asymmetrical. Based on these phase pattern characteristics, researchers tend to determine the orientation of a user array antenna by searching over a set of orientation candidates for the desired value closest to the true orientation. The search result is determined by comparing the relative positioning solutions between the user array antenna and a reference station. The proposed method combines two search strategies: spatial dimension search (SDS) and time dimension search (TDS). Furthermore, the proposed method is assessed in terms of the accuracy of orientation search results and relative positioning solutions with a short baseline at Curtin University, demonstrating the validity of the scheme. Multiple longer baselines formed by International GNSS Service (IGS) stations are also tested, and the results indicate that the proposed method is effective for baselines shorter than 2 km. Finally, to quantitatively study the type of phase pattern to which our method is applicable, we measure the characteristics of phase patterns with the peak value and a Frobenius norm-based asymmetry value. We also analyze the impact of the pattern characteristics on the performance of the proposed method. [ABSTRACT FROM AUTHOR]

With the development of new distributed cluster applications such as distributed radar and UGV cooperation, the concept of relative positioning springs upâ€"different from the classic positioning application, relative positioning focuses on the real-time determination of relative position relationships in multi-node networks without pre-deployment anchor, prior map and GNSS. Under such application conditions, relative localization also requires high real-time performance, and high accuracy in short baseline scenes, and can solve the positioning failure caused by occlusion and other factors. A high-precision relative positioning scheme based on UWB-INS fusion is proposed in this paper: a robust relative positioning algorithm based on iterative extended Kalman filter is optimized by modeling the system error state, and the adaptive residual elimination algorithm is introduced to ensure the robustness of positioning service. A series of experiments show that the scheme proposed in this paper can provide centimeter-level real-time positioning service for multi-node dynamic networks without anchors and maps, and is expected to become a potential effective relative positioning technology scheme. [ABSTRACT FROM AUTHOR]

Orbit determination of spacecraft in orbit has been mostly dependent on either GNSS satellite signals or ground station telemetry. Both methods present their limitations, however: GNSS signals can only be used effectively in earth orbit, and ground-based orbit determination presents an inherent latency that increases with the Earth–spacecraft distance. For spacecraft flying formations, an alternative method of orbit determination, independent of external signals, consists in the observation of the spacecraft's position with respect to the central body through the relative positioning history of the spacecraft within the formation. In this paper, the potential of the relative positioning method is demonstrated in the context of the SunRISE mission, and compared with the mission's previously proposed orbit determination methods. An optimization study is then made to find the optimal placement of a new spacecraft in the formation so as to maximize the positioning accuracy of the system. Finally, the possibility of removing part of the system's relative bearing measurements while maintaining its observability is also studied. The resulting system is found to be observable, but ill-conditioned. • Near-GEO Spacecraft formation orbit determination through relative positioning. • Absolute state estimation improves relative positioning accuracy. • Addition of a new observability optimized spacecraft to improve positioning accuracy. • System partially deprived of relative bearing is observable, but ill-conditioned. [ABSTRACT FROM AUTHOR]

The single-baseline solution (SBS) model has been widely adopted by the existing global navigation satellite system (GNSS) deformation monitoring systems due to its theoretical simplicity and ease of implementation. However, the SBS model neglects the mathematical correlation between baselines, and the accuracy and reliability can be degraded for baselines with long length, large height difference or frequent satellite signal occlusion. When monitoring large-area ground settlement or long-spanned linear objects such as bridges and railroads, multiple reference stations are frequently utilized, which can be exploited to improve the monitoring performance. Therefore, this paper evaluates the multi-baseline solution (MBS) model, and constrained-MBS (CMBS) model that has a prior constraint of the spatial-correlated tropospheric delay. The reliability and validity of the MBS model are verified using GPS/BDS datasets from ground settlement deformation monitoring with a baseline length of about 20 km and a height difference of about 200 m. Numerical results show that, compared with the SBS model, the MBS model can reduce the positioning standard deviation (STD) and root-mean-squared (RMS) errors by up to (47.4/51.3/66.2%) and (56.9/60.4/58.4%) in the north/east/up components, respectively. Moreover, the combined GPS/BDS positioning performance for the MBS model outperforms the GPS-only and BDS-only positioning models, with an average accuracy improvement of about 13.8 and 25.8%, with the highest accuracy improvement of about 41.6 and 43.8%, respectively. With the additional tropospheric delay constraint, the CMBS model improves the monitoring precision in the up direction by about 45.0%. [ABSTRACT FROM AUTHOR]

Duty factor (DF) and step frequency (SF) are key running pattern determinants. However, running patterns may change with speed if DF and SF changes are inconsistent across speeds. We examined whether the relative positioning of runners was consistent: 1) across five running speeds (10–18 km/h) for four temporal variables [DF, SF, and their subcomponents: contact ( t c ) and flight ( t f ) time]; and 2) across these four temporal variables at these five speeds. Three-dimensional whole-body kinematics were acquired from 52 runners, and deviations from the median for each variable (normalised to minimum-maximum values) were extracted. Across speeds for all variables, correlations on the relative positioning of individuals were high to very high for 2–4 km/h speed differences, and moderate to high for 6–8 km/h differences. Across variables for all speeds, correlations were low between DF-SF, very high between DF- t f , and low to high between DF- t c , SF- t c , and SF- t f . Hence, the consistency in running patterns decreased as speed differences increased, suggesting that running patterns be assessed using a range of speeds. Consistency in running patterns at a given speed was low between DF and SF, corroborating suggestions that using both variables can encapsulate the full running pattern spectrum. [ABSTRACT FROM AUTHOR]

The carrier-based kinematic-to-kinematic relative positioning can obtain the precise baseline between two moving stations, which greatly expands the application field of dynamic relative positioning. However, the relative positioning performance is degraded greatly with low fixation rate of ambiguity with low-cost receivers. Especially, in the complex dynamic environment, ambiguity resolution effect is influenced by the satellite signal blocked, multipath outlier, and abnormal state prediction. Aiming at the problems, a novel inertial navigation system–aided robust adaptive filtering ambiguity resolution model is proposed. In addition, a hierarchical filtering strategy is developed to eliminate ambiguity parameters in BeiDou navigation satellite system/inertial navigation system tightly coupled integrated system. Finally, the precise relative position can be calculated with the "best" ambiguity solution. Both experiments with static data and field vehicle test were carried out to evaluate the algorithm efficiency in different data configurations. The results indicate that IRAFAR-TCRP method can effectively suppress the influence of observation outliers and model prediction abnormalities, which improves the success rate of ambiguity resolution, raises the accuracy as well as the continuity of relative positioning. The success rate of ambiguity resolution with single-frequency BeiDou navigation satellite system can reach 90% in the gross error and abnormal disturbance environments and centimeter-level accuracy can be achieved. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Compared to traditional vehicles, the underwater bionic manta ray vehicle (UBMRV) is highly maneuverable, has strong concealment, and is an emerging research field in underwater vehicles. Based on the completion of the single-body research, it is crucial to research the swarm of UBMRVs for the implementation of complex tasks, such as large-scale underwater detection. The relative positioning capability of the UBMRV is the key to realizing a swarm, especially when underwater acoustic communications are delayed. To solve the real-time relative positioning problem between individuals in the UBMRV swarm, this study proposes a relative positioning method based on the combination of the improved object detection algorithm and binocular distance measurement. To increase the precision of underwater object detection in small samples, this paper improves the original YOLOx algorithm. It increases the network’s interest in the object area by adding an attention mechanism module to the network model, thereby improving its detection accuracy. Further, the output of the object detection result is used as the input of the binocular distance measurement module. We use the ORB algorithm to extract and match features in the object-bounding box and obtain the disparity of the features. The relative distance and bearing information of the target are output and shown on the image. We conducted pool experiments to verify the proposed algorithm on the UBMRV platform, proved the method’s feasibility, and analyzed the results.

Focusing on the dilemma that under the condition of satellite navigation signal void or extreme susceptibility to interference, indoor fast-moving robots and large-scale drone clusters flying outdoors must rely on high-precision and high-real time relative positioning to achieve motion navigation and control so that to avoid collisions between each individual, it is urgent to further explore methods with high real-time performance and high accuracy. Based on the in-depth study of the UWB ranging processing system for the process of “ ranging request-radio frequency module transmission-antenna delay correction-reverse transmission of the received signal”. Firstly, the system established a detailed sequence time analysis and estimation method. compared with the commonly used double-sided two-way ranging(DS-TWR), it shortened the ranging time, increased the survey frequency, and decreased the number of error frames. Secondly, the system used an active temperature-compensation crystal oscillator to correct the clock, constructed an empirical model for correcting the error in the near-field area of the antenna radiation, and introduced the Kalman filter to conduct fusion estimation to the ranging results, which reduced the measurement error. reaches ± 5 cm equivalent to that of the DS-TWR, and the ranging time is less than 5 ms. [ABSTRACT FROM AUTHOR]

• A lightweight onboard measurement system for micro-UAV swarms based on sensor fusion and neural networks is proposed. • A collaborative relative positioning method based on DGO and DEKF is developed. • The system is implemented on a 150 g micro-UAV platform, demonstrating high precision and real-time capability. In recent years, the swarm of micro Unmanned Aerial Vehicles (micro-UAVs) is becoming a research hotspot. As the basis of swarm autonomous flight, traditional relative positioning methods typically rely on external base stations or onboard sensors of high power consumption, the vast majority of which are not feasible on resource-constrained micro-UAVs flying in a complex environment. This article proposes a lightweight relative positioning system implemented on a micro-UAV platform weighing 150 g. We have developed a distributed graph optimization (DGO) scheme fusing the results of the onboard Ultra-Wideband (UWB) module, camera, and inertial sensors. A Delay Extended Kalman Filter (DEKF) is utilized to mitigate communication delays and enhance accuracy. This scheme enables micro-UAVs to collaboratively compute the relative position graph in real-time. Through real-world experiments, our system has achieved a relative positioning accuracy of 0.167 m, exhibiting significant potential for future swarm applications. [ABSTRACT FROM AUTHOR]

During the last decade Global Positioning System (GPS) Continuous Operating Reference Stations networks have become a new important data source for meteorology. This has dramatically improved the ability to remotely sense the atmosphere under the influence of severe mesoscale and synoptic systems. The zenith tropospheric delay (ZTD) is one of the atmospheric variables continuously observed, and its horizontal variations, the horizontal tropospheric gradients, are routinely computed nowadays within the dual‐frequency GPS processing, but their interpretation and relationship with the weather is still an open question. The purpose of this paper is to contribute in this direction by studying the effect that Hurricane Harvey had on the spatial and temporal behavior of the ZTDs and gradients, when it reached Texas coast, during 18–31 August 2017. The results show that ZTD time series present a clear and rapid increase larger than 10 cm in a few hours when the hurricane reached the area. Gradients behaviors show that the hurricane also produced significant changes on them, since the magnitude and predominant directions before and after the hurricane arrived are completely different. Noticeably, the gradient vectors before the landing are consistently related to the horizontal winds and pressure fields. In this manuscript we demonstrate that the ZTD gradients can show a consistent signature under severe weather events, strongly suggesting their potential application for short‐term weather forecasting.

Galileo navigation system has significantly expanded satellite constellation over the last few years and now serves with 22 satellites in three orbital planes. The development of the Galileo is not only limited to the number of satellites in the space segment, but also the quality of the IGS MGEX products produced by different Analysis Centers has increased gradually and brought up the topic of investigation of the accuracy and precision of the Galileo-only positioning. However, almost all of the studies on Galileo-only positioning in the literature have been performed with Precise Point Positioning method and comprehensive studies with the relative positioning technique are quite a few. In this study, the development of the accuracy and precision of the Galileo from 2016 to the end of 2019 (4 years) has been investigated for the first time by means of relative positioning technique. For this purpose, data of eight GNSS stations selected from the IGS MGEX network were obtained and analyzed with GAMIT/GLOBK software as GPS-only and Galileo-only, separately. Using the daily solutions obtained from both navigation systems, time series were produced and the differences between them were compared in order to reveal the Galileo accuracy. According to the results, the accuracy of the Galileo was variable during the experiment and it could be examined in three periods. The averages of 3D positioning error in the first period (2016-2017) is 14.92 mm and improved by 56%, and decreased to 6.55 mm in the second period (2017-2018), and then improvement at 23% in the third period (2018 to the end of 2019) and decrescent to level of 5.13 mm on average for all stations. Similarly, standard deviations obtained from Galileo increased during the experiment and during the 3rd period it was closed to a few mm with GPS. It was concluded that the accuracy and precision of the Galileo showed parallel development and the development was directly related to the number of satellites in the Galileo constellation, and as a result, nowadays, Galileo-derived coordinates are close to the coordinates obtained from the GPS by a few mm. [ABSTRACT FROM AUTHOR]

Virtual assembly (VA) is a method to simulate the physical assembly (PA) of scanned parts. Small local part deviations can accumulate to large assembly deviations limiting the product quality. The propagation of geometrical deviations onto the assembly is a crucial step in tolerance management to assess the assembly quality. Current approaches for VA do not sufficiently consider the physical joining process. Therefore, the propagated assembly geometry may deviate strongly from the PA. In the state of the art, only specific and complex methods for particular joining processes are known. In this paper, the concept of Surrogate Models (SMs) is introduced, representing the connection between part and assembly geometries for particular joining processes. A Surrogate Modelling Toolbox (SMT) is developed that is intended to cover the variety of joining processes by the implementation of suitable SMs. A particular SM is created by the composition of suitable Surrogate Operations (SOs). An open list of SOs is presented. The composition of a SM is studied for a laser welding process of two polymer components. The resulting VA is compared to the PA in order to validate the developed model and is quantified by the exploitation ratio R. [ABSTRACT FROM AUTHOR]

Precise positioning is a prerequisite for airborne high-quality magnetic data surveying. Modern airborne magnetic surveying platforms are usually composed of two parts: an aerial vehicle and a suspended magnetometer assembly. This configuration poses a challenge for georeferencing of data since the sensitivity of the magnetic sensors prohibits the placement of electronic positioning sensors directly on the assembly. This work focuses on the development of a positioning approach using computer vision and sensor fusion, which provides a globally referenced position, as well as preserving the electromagnetic isolation for the magnetic sensors. For the sake of magnetic isolation, the proposed approach estimates the position by fusing the relative position between the UAV and the magnetic sensor as well as the global position of the UAV itself. Compared with other solutions, the proposed solutions present two main advantages: (1) It is passive and provides excellent magnetic isolation to the sensitive magnetic sensor; (2) it can continue to provide the position of the magnetometer even if the sensor is in certain environments where direct positioning using global navigation satellite system or visual odometry would fail. We present the complete solution, including the temporal/spatial alignment of the GNSS/INS system and camera, front-end vision-based detection and tracking, and back-end position estimation and optimization. For performance evaluation, we use a commercial GNSS/INS system on the assembly as a reference and then compare the result of the proposed method with respect to the reference. The results from several real flight tests show that the vertical and horizontal accuracy of the proposed method is at centimeter level and decimeter level, respectively, which comes close to the performance of the reference system. [ABSTRACT FROM AUTHOR]

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In this study, the double-differenced positioning bias of different frequency observations (PBDFO) in a code division multiple access (CDMA) signal system is analyzed based on real-time kinematic (RTK) positioning using raw observations at different frequencies. To test and verify the existence of the DD PBDFO, a set of ultra-short baseline (25 m) GNSS observation data were collected. The stations NT03 and NT04 were located in Xi'an City, Shaanxi Province. The observation time ranged from UTC 11:00 to 17:00 for 10 consecutive days from DOY 012 to DOY 021 in 2017. The results showed that there are mm–cm differences in the RTK solution results for the raw observations at different frequencies, the mean bias and the standard deviation (STD) of the positioning results of GPS L1 and L2 in the East, North, and Up components are (−0.595 mm, 1.35 mm, −0.133 mm) and (3.11 mm, 3.32 mm, 6.44 mm), respectively, but for BDS B1 and B2, the corresponding values are (2.16 mm, −4.24 mm, 9.48 mm) and (3.35 mm, 4.85 mm, 7.41 mm), respectively. The PBDFO between GPS L1 and L2 frequencies is lower in magnitude and more stable than the PBDFO between BDS B1 and B2 frequencies, the mean and the STD of the PBDFO between GPS L1 and L2 obtained from the parameter estimation method in the East, North, and Up components are (−0.606 mm, 1.03 mm, −0.217 mm) and (0.162 mm, 0.169 mm, 0.276 mm), respectively; but for BDS B1 and B2, the corresponding values are (3.00 mm, −3.83 mm, 8.59 mm) and (0.375 mm, 1.14 mm, 2.47 mm), respectively. Both PBDFO correction model and parameter estimation approach can achieve consistency in terms of the RTK positioning results at different frequencies. The double-differenced (DD) PBDFO obtained from the direct difference method and parameter estimation approach are consistent and relatively stable for the same period of continuous days. [ABSTRACT FROM AUTHOR]