Search Results (18)

The phenomenon of land subsidence has been demonstrated to exert a considerable influence on GPS observations. However, to date, no study which has successfully removed the impact of land subsidence on GPS horizontal motion has been conducted. We developed an original sequence-to-sequence deep learning model for the elimination of the impact of land subsidence on GPS horizontal motion, employing gated recurrent units. The model is capable of predicting the horizontal motion of the target GPS station with the impact of land subsidence removed by learning the implicit relationship between the horizontal motion and vertical data of the station. A local model was constructed for each GPS station in the Tianjin subsidence area, and the corresponding dataset was generated for the purposes of model training and testing. The vertical data, with the impact of land subsidence removed, were employed as model inputs for the purpose of predicting the horizontal motion of the same station, with the impact of land subsidence similarly removed. The results demonstrate that following the removal of the impact of land subsidence, the dispersion of GPS horizontal motion within the Tianjin subsidence area is markedly diminished, and the horizontal motion trend exhibits greater consistency with that observed at neighboring stations in non-subsidence regions. The impact of land subsidence on GPS horizontal motion exhibits variability across different regions of the Tianjin subsidence area and among disparate stations. Full article

To achieve the efficient and precise control of autonomous underwater vehicles (AUVs) in dynamic ocean environments, this paper proposes an innovative Gaussian-Process-based Model Predictive Control (GP-MPC) method. This method combines the advantages of Gaussian process regression in modeling uncertainties in nonlinear systems, and MPC’s constraint optimization and real-time control abilities. To validate the effectiveness of the proposed GP-MPC method, its performance is first evaluated for trajectory tracking control tasks through numerical simulations based on a 6-degrees-of-freedom, fully actuated, AUV dynamics model. Subsequently, for 3D scenarios involving static and dynamic obstacles, an AUV horizontal plane decoupled motion model is constructed to verify the method’s obstacle avoidance capability. Extensive simulation studies demonstrate that the proposed GP-MPC method can effectively manage the nonlinear motion constraints faced by AUVs, significantly enhancing their intelligent obstacle avoidance performance in complex dynamic environments. By effectively handling model uncertainties and satisfying motion constraints, the GP-MPC method provides an innovative and efficient solution for the design of AUV control systems, substantially improving the control performance of AUVs. Full article

Global Navigation Satellite System (GNSS) technology supports all phases of maritime navigation and serves as an integral component of the Automatic Identification System (AIS) and, by extension, Vessel Traffic Service (VTS) systems. However, the accuracy of standalone GNSS is often insufficient for specific operations. To address this limitation, various regional and local-area solutions have been developed, such as Differential GNSS (DGNSS), Satellite Based Augmentation Service (SBAS) and Real Time Kinematic (RTK) techniques. A notable development in this field is the recent introduction of the Galileo High-Accuracy Service (HAS), which saw its initial service declared operational by the European Commission (EC) on 24 January 2023. Galileo HAS provides high-accuracy Precise Point Positioning (PPP) corrections (orbits, clocks and signal biases) for Galileo and GPS, enhancing real-time positioning performance at no additional cost to users. This article presents the results of the first Galileo HAS testing campaign conducted at sea using a buoy-laying vessel temporarily equipped with a Galileo HAS User Terminal. The results presented in this Article include accuracy and position availability performance achieved using the Galileo HAS User Terminal. The article also highlights challenges posed by high-power radio-frequency interference, which likely originated from the Long-Range Identification and Tracking (LRIT) system antenna on board the vessel. Furthermore, the article provides additional assessments for different phases of navigation, demonstrating better performance in slow-motion scenarios, particularly relevant to mooring and pilotage applications. In these scenarios, values for horizontal accuracy reached 0.22 m 95% and 0.13 m 68% after removing interference periods. These results are in line with the expectations outlined in the Galileo HAS Service Definition Document (SDD). Full article

Quantitative information on how well a horse clears a jump has great potential to support the rider in improving the horse’s jumping performance. This study investigated the validation of a GPS-based inertial measurement unit, namely Alogo Move Pro, compared with a traditional optical motion capture system. Accuracy and precision of the three jumping characteristics of maximum height (Zmax), stride/jump length (lhorz), and mean horizontal speed (vhorz) were compared. Eleven horse–rider pairs repeated two identical jumps (an upright and an oxer fence) several times (n = 6 to 10) at different heights in a 20 × 60 m tent arena. The ground was a fiber sand surface. The 24 OMC (Oqus 7+, Qualisys) cameras were rigged on aluminum rails suspended 3 m above the ground. The Alogo sensor was placed in a pocket on the protective plate of the saddle girth. Reflective markers placed on and around the Alogo sensor were used to define a rigid body for kinematic analysis. The Alogo sensor data were collected and processed using the Alogo proprietary software; stride-matched OMC data were collected using Qualisys Track Manager and post-processed in Python. Residual analysis and Bland–Altman plots were performed in Python. The Alogo sensor provided measures with relative accuracy in the range of 10.5–20.7% for stride segments and 5.5–29.2% for jump segments. Regarding relative precision, we obtained values in the range of 6.3–14.5% for stride segments and 2.8–18.2% for jump segments. These accuracy differences were deemed good under field study conditions where GPS signal strength might have been suboptimal. Full article

The paper focuses on the issue of collaborative control of a two quadrotor (Unmanned Aerial Vehicle QDR) system. In particular, two quadrotors perform the task of horizontally transporting a long payload along a predefined trajectory. A leader–follower method is used to synchronize the motion of both QDRs. Conventional PD controllers drive the motion of the leader QDR-L to follow a predefined trajectory. To control a follower QDR-F drive, in the case of indoor applications, a Position Feedback Controller approach (PFC) can be used. To control the QDR-F, the PFC system uses the position information of QDR-L and the required accurate tracking cameras. In our solution, outdoor applications are considered, and usage of the Global Positioning System (GPS) is needed. However, GPS errors can adversely affect the system’s stability. The Force Feedback Controller approach (FFC) is therefore implemented to control the QDR-F motion. The FFC system assumes a rigid gripping of payload by both QDRs. The QDR-F collaborative motion is controlled using the feedback contact forces and torques acting on it due to the motion of the QDR-L. For FFC implementation, the principle of admittance control is used. The admittance controller simulates a virtual “mass-spring-damper” system and drives the motion of the QDR-F according to the contact forces. With the FFC control scheme, the follower QDR-F can be controlled without using the QDR-L positional feedback and the GPS. The contribution to the quality of payload transportation is the novelty of the article. In practice, one of the requirements may be to maintain the horizontal position of the payload. In this paper, an original solution is presented to minimize the horizontal position difference of both QDRs. A new procedure of the transfer admittance controller adaptation according to the mass of the transported payload is designed. The adaptive admittance FFC system is implemented in a Matlab-Simulink environment. The effectiveness of its trajectory tracking and horizontal stabilization functions for variations of the payload mass are demonstrated by numerical calculations. Full article

Interferometric synthetic aperture radar (InSAR) technology can obtain one-dimensional surface displacements in the radar line of sight (LOS). In the field of mining subsidence, large 3D movements often occur at the same time, and hence InSAR derived one-dimensional LOS displacements can hardly reflect the actual surface motion in mining areas. To realize the monitoring of three-dimensional large surface displacements in mining areas, a method for monitoring three-dimensional large surface displacements in mining areas that combines SAR pixel offset tracking (OT) and an improved mining subsidence model is proposed in this article. First, a new functional relationship between surface subsidence and horizontal movement combined with the characteristics of the overburden rock stress and the deformation characteristics of the fractured rock mass in coal mining areas is established. Then, a three-dimensional surface deformation model is established based on the proposed relationship between surface subsidence and horizontal movement and the radar projection equation, and finally, the optimal parameters of the deformation model are inverted iteratively using LOS deformation results obtained by OT method to retrieve the three-dimensional large displacements of the surface. The significant advantage of the method proposed in this article is that it can accurately acquire the 3D large surface displacements using only two SAR amplitude images with the same imaging geometry. To verify the accuracy and reliability of the proposed algorithm, two scenes of high-resolution spotlight TerraSAR-X images are used in this paper to conduct a three-dimensional surface displacement monitoring experiment on a working panel in the Daliuta mining area in Shaanxi Province, China, based on the proposed method. Experimental monitoring results show that the maximum surface subsidence is approximately 4.5 m, and the maximum horizontal movements in the strike and dip directions are approximately 1.4 m and 1.2 m, respectively. Using GPS measurements to verify the monitoring results, the root mean square error (RMSE) of vertical subsidence is 6.8 cm, and the RMSE of horizontal movement is 7.1 cm. Compared with those in the original mining subsidence model, the accuracies of vertical subsidence and horizontal movement in the proposed model are increased by 28.2% and 37.5%, respectively, which proves the reliability and accuracy of the proposed method. Full article

The 2015 Mw 7.8 Gorkha, Nepal, earthquake occurred in the central Himalayan collisional orogenic belt, which demonstrated complex fault kinematics and significant surface deformation. The coseismic deformation has been well documented by previous studies using Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR) data. However, due to some limitations of spatially sparse GPS stations and InSAR only-one-dimensional observation in the line-of-sight (LOS) direction, the complete distribution and detailed spatial variation of the three-dimensional surface deformation field are still not fully understood. In this study, we reconstructed the three-dimensional coseismic deformation fields using multi-view InSAR observations and investigated the refined surface deformation characteristics during this event. We firstly obtained four ascending and descending InSAR coseismic deformation maps from both Sentinel-1A/B and ALOS-2 data. Secondly, we obtained the synthetic north-south deformation field from our best-fitting slip distribution inversions. Finally, we calculated three-dimensional deformation fields, which were consistent with coseismic GPS displacements but with higher resolution. We found that the surface deformation is dominated by horizontal southward motion and vertical uplift and subsidence, with minor east-west deformation. In the north-south direction, the whole deformation area reaches at least 150 × 150 km with a maximum displacement of ~1.5 m. In the vertical direction, two areas, including uplift in the south and subsidence in the north, are mapped with a peak displacement of 1.5 and −1.0 m, respectively. East-west deformation presented a four-quadrant distribution with a maximum displacement of ~0.6 m. Complex thrusting movement occurred on the seismogenic fault; overall, there was southward push motion and wave-shaped fold motion. Full article

The combination of biomechanics and inertial pedestrian navigation research provides a very promising approach for pedestrian positioning in environments where Global Positioning System (GPS) signal is unavailable. However, in practical applications such as fire rescue and indoor security, the inertial sensor-based pedestrian navigation system is facing various challenges, especially the step length estimation errors and heading drift in running and sprint. In this paper, a trinal-node, including two thigh-worn inertial measurement units (IMU) and one waist-worn IMU, based simultaneous localization and occupation grid mapping method is proposed. Specifically, the gait detection and segmentation are realized by the zero-crossing detection of the difference of thighs pitch angle. A piecewise function between the step length and the probability distribution of waist horizontal acceleration is established to achieve accurate step length estimation both in regular walking and drastic motions. In addition, the simultaneous localization and mapping method based on occupancy grids, which involves the historic trajectory to improve the pedestrian’s pose estimation is introduced. The experiments show that the proposed trinal-node pedestrian inertial odometer can identify and segment each gait cycle in the walking, running, and sprint. The average step length estimation error is no more than 3.58% of the total travel distance in the motion speed from 1.23 m/s to 3.92 m/s. In combination with the proposed simultaneous localization and mapping method based on the occupancy grid, the localization error is less than 5 m in a single-story building of 2643.2 m${}^2$. Full article

This contribution presents and assesses the methodology aiming at the characterization of the structural vibrations with high-rate GNSS measurements. As commonly employed precise point positioning (PPP) based on ionosphere-free linear combination of undifferenced signals may not meet the high requirements in terms of displacement precision, a modified processing strategy has been proposed. The algorithms were implemented in the own-developed GNSS processing software and validated using the designed experiment. For this purpose, we have set up a field experiment taking advantage of the prototype shake-table, which simulated the dynamic horizontal displacements of the GNSS antenna. The device ensured a periodic motion of the antenna with modifiable characteristics, namely amplitude and frequency. In this experiment, we have set the amplitudes from 1.5 to 9 mm and the frequency to 3.80 Hz. As a dataset, we have used 100 Hz GPS, Galileo, and BDS measurements. The results confirmed a high applicability of the enhanced PPP processing strategy for precise displacement detection. Specifically, it was feasible to obtain the dynamic displacements with precision at the level of millimeters. The differences between the PPP-derived amplitude and the true amplitude of the simulated displacements were in the range of 0.5–1.3 mm, whereas the difference between the detected and benchmark frequency did not exceed 0.026 Hz. Hence, the proposed methodology allows meeting the specific demands of structural displacement monitoring. Full article

Tree heights are the principal variables for forest plantation inventory. The increasing availability of high-resolution three-dimensional (3D) point clouds derived from low-cost Unmanned Aerial Vehicle (UAV) and modern photogrammetry offers an opportunity to generate a Canopy Height Model (CHM) in the mountainous areas. In this paper, we assessed the capabilities of tree height estimation using UAV-based Structure-from-Motion (SfM) photogrammetry and Semi-Global Matching (SGM). The former is utilized to generate 3D geometry, while the latter is used to generate dense point clouds from UAV imagery. The two algorithms were coupled with a Radial Basis Function (RBF) neural network to acquire CHMs in mountainous areas. This study focused on the performance of Digital Terrain Model (DTM) interpolation over complex terrains. With the UAV-based image acquisition and image-derived point clouds, we constructed a 5 cm-resolution Digital Surface Model (DSM), which was assessed against 14 independent checkpoints measured by a Real-Time Kinematic Global Positioning System RTK GPS. Results showed that the Root Mean Square Errors (RMSEs) of horizontal and vertical accuracies are approximately 5 cm and 10 cm, respectively. Bare-earth Index (BEI) and Shadow Index (SI) were used to separate ground points from the image-derived point clouds. The RBF neural network coupled with the Difference of Gaussian (DoG) was exploited to provide a favorable generalization for the DTM from 3D ground points with noisy data. CHMs were generated using the height value in each pixel of the DSM and by subtracting the corresponding DTM value. Individual tree heights were estimated using local maxima algorithm under a contour-surround constraint. Two forest plantations in mountainous areas were selected to evaluate the accuracy of estimating tree heights, rather than field measurements. Results indicated that the proposed method can construct a highly accurate DTM and effectively remove nontreetop maxima. Furthermore, the proposed method has been confirmed to be acceptable for tree height estimation in mountainous areas given the strong linear correlation of the measured and estimated tree heights and the acceptable t-test values. Overall, the low-cost UAV-based photogrammetry and RBF neural network can yield a highly accurate DTM over mountainous terrain, thereby making them particularly suitable for rapid and cost-effective estimation of tree heights of forest plantation in mountainous areas. Full article

We have developed a Stable Caribbean Reference Frame 2018 (CARIB18) using long-term continuous observations from 18 continuously operating Global Positioning System (GPS) stations fixed on the margins of the stable portion of the Caribbean plate. The frame stability of CARIB18 is approximately 0.7 mm/year in the horizontal direction and 0.9 mm/year in the vertical direction. A method that employs a total of seven parameters for transforming positional time series from a global reference frame (IGS14) to a regional reference frame is introduced. The major products from this study include the seven parameters for realizing CARIB18 coordinates and three-component site velocities of 250 continuous GPS stations (>3 years) with respect to CARIB18. Geological hazard monitoring using GPS has traditionally been performed using the carrier-phase differential method that requires single or multiple reference stations to be simultaneously operated in the field. CARIB18 allows for precise geological hazard monitoring using stand-alone GPS, which substantially reduces field costs and simplifies logistics for long-term geological hazard monitoring. Applications of CARIB18 in plate motion, post-seismic, and volcano monitoring and research are demonstrated in this article. The regional reference frame will be periodically updated every few years with more reference stations and longer periods of observations to mitigate the degradation of the frame over time and will be synchronized with the updates of the International GNSS Service (IGS) IGS reference frame. Full article

This paper introduces a new method which facilitate the use of smartphones as a handheld low-cost mobile mapping system (MMS). Smartphones are becoming more sophisticated and smarter and are quickly closing the gap between computers and portable tablet devices. The current generation of smartphones are equipped with low-cost GPS receivers, high-resolution digital cameras, and micro-electro mechanical systems (MEMS)-based navigation sensors (e.g., accelerometers, gyroscopes, magnetic compasses, and barometers). These sensors are in fact the essential components for a MMS. However, smartphone navigation sensors suffer from the poor accuracy of global navigation satellite System (GNSS), accumulated drift, and high signal noise. These issues affect the accuracy of the initial Exterior Orientation Parameters (EOPs) that are inputted into the bundle adjustment algorithm, which then produces inaccurate 3D mapping solutions. This paper proposes new methodologies for increasing the accuracy of direct geo-referencing of smartphones using relative orientation and smartphone motion sensor measurements as well as integrating geometric scene constraints into free network bundle adjustment. The new methodologies incorporate fusing the relative orientations of the captured images and their corresponding motion sensor measurements to improve the initial EOPs. Then, the geometric features (e.g., horizontal and vertical linear lines) visible in each image are extracted and used as constraints in the bundle adjustment procedure which correct the relative position and orientation of the 3D mapping solution. Full article

Most location-based services are based on a global positioning system (GPS), which only works well in outdoor environments. Compared to outdoor environments, indoor localization has created more buzz in recent years as people spent most of their time indoors working at offices and shopping at malls, etc. Existing solutions mainly rely on inertial sensors (i.e., accelerometer and gyroscope) embedded in mobile devices, which are usually not accurate enough to be useful due to the mobile devices’ random movements while people are walking. In this paper, we propose the use of shoe sensing (i.e., sensors attached to shoes) to achieve 3D indoor positioning. Specifically, a short-time energy-based approach is used to extract the gait pattern. Moreover, in order to improve the accuracy of vertical distance estimation while the person is climbing upstairs, a state classification is designed to distinguish the walking status including plane motion (i.e., normal walking and jogging horizontally), walking upstairs, and walking downstairs. Furthermore, we also provide a mechanism to reduce the vertical distance accumulation error. Experimental results show that we can achieve nearly 100% accuracy when extracting gait patterns from walking/jogging with a low-cost shoe sensor, and can also achieve 3D indoor real-time positioning with high accuracy. Full article

This paper proposes a method for combining stereo visual odometry, Light Detection And Ranging (LiDAR) odometry and reduced Inertial Measurement Unit (IMU) including two horizontal accelerometers and one vertical gyro. The proposed method starts with stereo visual odometry to estimate six Degree of Freedom (DoF) ego motion to register the point clouds from previous epoch to the current epoch. Then, Generalized Iterative Closest Point (GICP) algorithm refines the motion estimation. Afterwards, forward velocity and Azimuth obtained by visual-LiDAR odometer are integrated with reduced IMU outputs in an Extended Kalman Filter (EKF) to provide final navigation solution. In this paper, datasets from KITTI (Karlsruhe Institute of Technology and Toyota technological Institute) were used to compare stereo visual odometry, integrated stereo visual odometry and reduced IMU, stereo visual-LiDAR odometry and integrated stereo visual-LiDAR odometry and reduced IMU. Integrated stereo visual-LiDAR odometry and reduced IMU outperforms other methods in urban areas with buildings around. Moreover, this method outperforms simulated Reduced Inertial Sensor System (RISS), which uses simulated wheel odometer and reduced IMU. KITTI datasets do not include wheel odometry data. Integrated RTK (Real Time Kinematic) GPS (Global Positioning System) and IMU was replaced by wheel odometer to simulate the response of RISS method. Visual Odometry (VO)-LiDAR is not only more accurate than wheel odometer, but it also provides azimuth aiding to vertical gyro resulting in a more reliable and accurate system. To develop low-cost systems, it would be a good option to use two cameras plus reduced IMU. The cost of such a system will be reduced than using full tactical MEMS (Micro-Electro-Mechanical Sensor) based IMUs because two cameras are cheaper than full tactical MEMS based IMUs. The results indicate that integrated stereo visual-LiDAR odometry and reduced IMU can achieve accuracy at the level of state of art. Full article

In southern Tibet, ongoing vertical and horizontal motions due to the collision between India and Eurasia are monitored by large numbers of global positioning system (GPS) continuous and campaign sites installed in the past decade. Displacements measured by GPS usually include tectonic deformation as well as non-tectonic, time-dependent signals. To estimate the regional long-term tectonic deformation using GPS more precisely, seasonal elastic deformation signals associated with surface loading must be removed from the observations. In this study, we focus on seasonal variation in vertical and horizontal motions of southern Tibet by performing a joint analysis of GRACE (Gravity Recovery and Climate Experiment) and GPS data, not only using continuous sites but also GPS campaign-mode sites. We found that the GPS-observed and GRACE-modeled seasonal oscillations are in good agreements, and a seasonal displacement model demonstrates that the main reason for seasonal variations in southern Tibet is from the summer monsoon and its precipitation. The biggest loading appears from July to August in the summer season. Vertical deformations observed by GPS and modeled by GRACE are two to three times larger than horizontal oscillations, and the north components demonstrate larger amplitudes than the east components. We corrected the GPS position time series using the GRACE-modeled seasonal variations, which gives significant reductions in the misfit and weighted root-mean-squares (WRMS). Misfit ( χ 2 divided by degree of freedom) reductions for campaign sites range between 20% and 56% for the vertical component, and are much smaller for the horizontal components. Moreover, time series of continuous GPS (cGPS) sites near the 2015 Nepal earthquakes must be corrected using appropriate models of seasonal loading for analyzing postseismic deformation to avoid biasing estimates of the postseismic relaxation. Full article