Global Navigation Satellite System Data Research Articles

A Geodynamic Investigation of Plume‐Lithosphere Interactions Beneath the East African Rift

AbstractThe force balance that drives and maintains continental rifting to breakup is poorly understood. The East African Rift (EAR) provides an ideal natural laboratory to elucidate the relative role of plate driving forces as only lithospheric buoyancy forces and horizontal mantle tractions act on the system. Here, we employ high‐resolution 3D thermomechanical models to test whether: (a) the anomalous, rift‐parallel surface deformation observed by Global Navigation Satellite System (GNSS) data in the EAR are driven by viscous coupling to northward mantle flow associated with the African Superplume, and (b) the African Superplume is the dominant source mechanism of anomalous rift‐parallel seismic anisotropy beneath the EAR. We calculate Lattice Preferred Orientations (LPO) and surface deformation from two types of mantle flow: (a) a scenario with multiple plumes constrained by shear wave tomography and (b) a single superplume model with northward boundary condition to simulate large‐scale flow. Comparison of calculated LPO with observed seismic anisotropy, and surface velocities with GNSS and plate kinematics reveal that there is a better fit with the superplume mantle flow model, rather than the tomography‐based (multiple plumes) model. We also find a relatively better fit spatially between observed seismic anisotropy and calculated LPO with the superplume model beneath northern and central EAR, where the superplume is proposed to be shallowest. Our results suggest that the viscous coupling of the lithosphere to northward mantle flow associated with the African Superplume drives most of the rift‐parallel deformation and is the dominant source of the first‐order pattern of the observed seismic anisotropy in the EAR.

Using the Global Navigation Satellite System and Precipitation Data to Establish the Propagation Characteristics of Meteorological and Hydrological Drought in Yunnan, China

AbstractAnalyzing the spatiotemporal characteristics and evolution of meteorological and hydrological droughts can reproduce the process of drought propagation, which helps reduce the impact of droughts and improve water resources management. While emerging studies have attempted to build drought severity index based on the cutting‐edge space geodetic observations, few have focused the propagation characteristics of meteorological to hydrological drought using geodetic data. In this paper, using global navigation satellite systems (GNSS) observations together with precipitation data, we systematically investigated drought propagation in the Yunnan province, Southwest China. We first identified seven meteorological and seven hydrological droughts in Yunnan from January 2011 to May 2021, and the meteorological drought was mainly concentrated in northern Yunnan lasting for 1–11 months. By contrast, hydrological droughts were more severe and larger than meteorological droughts, lasting for 2–16 months. The drought propagation time was 2–7 months, short in the southwest but long in the northeast. Water vapor, precipitation, and water storage demonstrated a spatiotemporal pattern of uneven distribution, with the red river fault (RRF) as the boundary, and their phase difference also presented notable regional differences, indicating that the RRF not only influences spatial variation of water resources but also affects drought propagation.

Retrieving Accurate Precipitable Water Vapor Based on GNSS Multi‐Antenna PPP With an Ocean‐Based Dynamic Experiment

AbstractAs an attractive technique for measuring water vapor, the Global Navigation Satellite System (GNSS) faces additional challenges in dynamic applications such as in the open sea. We present a new method of retrieving precipitable water vapor (PWV) based on GNSS multi‐antenna precise point positioning (PPP), which uses GNSS data from multiple antennas and incorporates the constraints of known baseline vector and common tropospheric delay. The 4‐day shipborne dynamic experiment along the China coast demonstrates that the baseline vector constraint shortens the convergence time of positioning and atmospheric parameters, and also slightly improves their accuracies. The common tropospheric delay constraint helps to provide compromised, more robust, and sometimes more accurate PWV estimates. An evaluation with radiosonde‐derived PWVs shows that the combination of the two constraints achieves the best accuracy reaching 4.2 mm. This method helps to expand GNSS meteorology to the vast ocean and benefits satellite altimetry and weather forecasting.

Three-dimensional ionospheric tomography based on compressed sensing

The insufficient number of low-elevation observations is a limitation of the three-dimensional ionospheric computer tomography (CT) based on the global navigation satellite system (GNSS). To solve this problem, accurate prior information on the regional ionosphere must be obtained. However, it is difficult to explicitly and accurately express prior ionospheric information. This study uses compressed sensing (CS) for ionospheric tomography for the first time. Specifically, the electron density obtained from the international reference ionosphere is used to build a dictionary to fully integrate the prior information into the dictionary. Then, the electron density is reconstructed by using the compressive sampling matching pursuit method. Subsequently, the GNSS data of China (Region I) and Europe (Region II) were utilized to validate this proposed method, and the results are compared with ionosonde observations. The mean and standard deviation (SD) of the difference with respect to the ionosonde result are 41 and 22 km, respectively. The mean and SD of relative deviation were 16% and 9%, respectively. In Region II, the mean and SD of the deviation between the reversed peak electron density and the result of the ionosonde were 1.9 × 1010 m−3 and 8.1 × 1010 m−3, respectively. The mean and SD of the relative deviation were 3% and 13%, respectively. The mean and SD of the peak height deviation were 33 and 19 km, and the mean and SD of the relative deviation were 11% and 7%. The electron density distribution and variation in these two regions showed a local time dependence, and the horizontal gradient of the electron density in the latitude was greater than that in the longitude. Moreover, CT by CS is efficient, taking about 6 s per inversion based on an desktop computer with 16 GB RAM and Intel (R) Core (TM) i7-8700 CPU.

Adaptive coloured noise multirate Kalman filter and its application in coseismic deformations

SUMMARYThe accuracy and sampling rate of a coseismic displacement and velocity waveform can be improved by fusing Global Navigation Satellite System (GNSS) and strong motion (SM) sensor data with a multirate Kalman filter. However, many studies have shown that the noise in GNSS coseismic waveforms includes coloured noise, and it is challenging to obtain proper GNSS and SM fusion results if the GNSS coloured noise is not accurately represented. In this paper, we propose a coloured noise multirate Kalman filter, which uses a stochastic model for modelling the GNSS coloured noise to achieve an accurate fusion of data from GNSS and SM for different sampling rates. In addition, we also propose an estimation method for the multirate Kalman filter stochastic model under coloured noise to achieve the optimal adaptive fusion of GNSS and SM data. After the reliability of the proposed method was confirmed using Monte Carlo simulations and earthquake engineering data tests, the proposed method was applied to data collected from the 2019 Mw 7.1 Ridgecrest earthquake and 2016 Mw 7.8 Kaikoura earthquake. The test results show that the proposed method can effectively fuse GNSS and SM data and accurately obtain broadband coseismic displacement and velocity waveforms.

Everywhere: A Framework for Ubiquitous Indoor Localization

Smartphones have become an integral part of daily human life and enable almost unlimited coverage of human mobility. Thus, collecting pervasive crowdsourced signatures is feasible. Autonomous localization of such signatures promotes the development of a self-deployable and ubiquitous indoor positioning system (IPS). However, previous crowdsourcing-based IPSs have not considered leveraging such data for developing ubiquitous IPSs. They have relied on methods for data selection and sources for localization adjustment that could work against realizing a ubiquitous system. In contrast, this study introduces a framework Everywhere that leverages crowdsourced data to develop a ubiquitous IPS and addresses existing challenges while developing such systems. Particularly, inertial data selection criteria are proposed to autonomously generate traces with better localization. Moreover, pervasive global navigation satellite system (GNSS) data are leveraged to adjust trace localization, while simultaneously introducing a deploying location (inside elevators) of one anchor node. The node surveys all the floors while reducing the localization error, especially for the buildings surrounded by GNSS-denied areas. Additionally, cumulative data densification is leveraged to realize pervasive resources within the building, thereby boosting trace adjustment and extending database spatial coverage. Furthermore, a better selection of neighboring fingerprints is proposed to enhance online fingerprinting. Such a framework can promote a ubiquitous IPS development for buildings regardless of whether they are surrounded by open sky or GNSS-denied areas.

Wavelet-like denoising of GNSS data through machine learning. Application to the time series of the Campi Flegrei volcanic area (Southern Italy)

The great potential of the Global Navigation Satellite System (GNSS) in monitoring ground deformation is widely recognized. As with other geophysical data, GNSS time series can be significantly noisy, hiding elusive ground deformation signals. Several denoising techniques have been proposed to improve the signal-to-noise ratio over the years. One of the most effective denoising techniques has been proved to be multi-resolution decomposition through the discrete wavelet transform. However, wavelet analysis requires long data sets to be effective, as well as long computation times, that hinder its use as a real or near real-time monitoring tool. We propose training by a Convolutional Neural Network (CNN) to perform the equivalent of wavelet analysis to overcome these limitations. Once trained, the CNN model provides answers within seconds, making it feasible as a real-time data analysis tool. Our Machine Learning algorithm is tested on daily GNSS time series collected in the Campi Flegrei caldera (Southern Italy), which is a highly volcanic risk area. Without significant gaps, the retrieved RMSE and R2 values vary in the ranges 0.65–0.98 and 0.06–0.52 cm, respectively. These results are encouraging, as they hint at the possibility of applying this methodology in more effective real-time monitoring solutions for active volcanoes.

Global statistical assessment of Haiyang-2B scanning microwave radiometer precipitable water vapor

Introduction: As a successor to Haiyang-2A (HY-2A), HY-2B is China’s second marine dynamic satellite. Equipped with a scanning microwave radiometer (SMR), it can measure the precipitable water vapor (PWV) over the oceans, providing valuable climate and weather insights. This study aims to evaluate the accuracy of HY-2B SMR PWV data from January 2019 to December 2021 using various validation methods.Methods: to validate HY-2B SMR PWV, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data, radiosonde data, and global navigation satellite system (GNSS) observations are used.Results: the validation shows that the HY-2B SMR PWV data agree well with the ERA5 PWV data, with a root mean square error (RMSE) of 1.61 mm and a mean value of 0.71 mm. However, RMSEs of approximately 3.5 mm are observed when comparing the HY-2B SMR PWV data to GNSS and radiosonde data, likely due to temporal and spatial gaps. Geographically, there are greater differences between the HY-2B SMR and ERA5 in lower-latitude areas. The reason could be attributed to a higher water vapor content and more frequent cloudy weather in the areas.Discussion: The results demonstrate that the HY-2B SMR PWV meets requirement (RMSE≤3.5 mm). The HY-2B SMR PWV has a high quality, with a slight observational drift of only 0.1 mm/year, but regular validation and calibration are still necessary.

Interchangeable Use of GNSS and Seismic Data for Rapid Earthquake Characterization: 2021 Chignik, Alaska, Earthquake

AbstractEarthquake magnitude estimation using peak ground velocities (PGVs) derived from Global Navigation Satellite Systems (GNSS) data has shown promise for rapid characterization of damaging earthquakes. Here, we examine the feasibility of using GNSS-derived velocity waveforms as interchangeable data for rapid magnitude and ground motion estimation that typically rely on strong-motion seismic records. Our study compares PGVs derived from high-rate GNSS to those computed from high-rate seismic records (strong-motion and velocity) at collocated and closely located stations. The recent 2021 Mw 8.2 Chignik earthquake in Alaska that was recorded on collocated GNSS and strong-motion sensors provides the perfect opportunity to compare the two data streams and their application in rapid response. The Chignik velocity records appear almost identical at collocated GNSS and strong-motion stations when observed at frequencies <0.25 Hz. GNSS and strong-motion derived velocity data are further employed to generate rapid estimates of PGV-derived moment magnitudes for the earthquake. The moment magnitude estimates from GNSS and joint GNSS and joint (GNSS and seismic) data are within ∼±0.4 magnitude units (Fang et al., 2020) of the final magnitude (Mw 8.2). ShakeMaps generated for the 2021 Chignik earthquake using GNSS and seismic PGVs show notable agreement between them, and show negligible shifts in PGV contours when collocated and closely located GNSS and seismic stations are substituted for one another. Therefore, we posit that GNSS is a powerful alternative or addition to seismic data and vice versa.

Global Navigation Satellite System-Based Retrieval of Precipitable Water Vapor and Its Relationship with Rainfall and Drought in Qinghai, China

Qinghai Province is situated deep in inland China, on the Qinghai-Tibet plateau, and it has unique climate change characteristics. Therefore, understanding the temporal and spatial distributions of water vapor in this region can be of great significance. The present study applied global navigation satellite system (GNSS) technology to retrieve precipitable water vapor (PWV) in Qinghai and analyzed its relationship with rainfall and drought. Firstly, radiosonde (RS) data is used to verify the precision of the surface pressure (P) and temperature (T) from the fifth-generation atmosphere reanalysis data set (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as the zenith troposphere delay (ZTD), calculated based on the data from continuously operating reference stations (CORS) in Qinghai. Secondly, a regional atmospheric weighted mean temperature (Tm) (QH-Tm) model was developed for Qinghai based on P, T, and relative humidity, as well as the consideration of the influence of seasonal changes in Tm. Finally, the PWV of each CORS in Qinghai was calculated using the GNSS-derived ZTD and ERA5-derived meteorological data, and its relationship with rainfall and drought was evaluated. The results show that the ERA5-derived P and T have high precision, and their average root mean square (RMS), mean absolute error (MAE) and bias were 1.06/0.85/0.01 hPa and 2.98/2.42/0.03 K, respectively. The RMS, MAE and bias of GNSS-derived ZTD were 13.2 mm, 10.3 mm and −1.8 mm, respectively. The theoretical error for PWV was 1.98 mm; compared with that of RS- and ERA5-derived PWV, the actual error was 2.69 mm and 2.16 mm, respectively. In addition, the changing trend of GNSS-derived PWV was consistent with that of rainfall events, and it closely and negatively correlated with the standardized precipitation evapotranspiration index. Therefore, the PWV retrieved from GNSS data in this study offers high precision and good feasibility for practical applications; thus, it can serve as a crucial tool for investigating water vapor distribution and climate change in Qinghai.

A Map-Matching Algorithm With Extraction of Multi-Group Information for Low-Frequency Data

The growing use of probe vehicles generates a huge number of global navigation satellite systems (GNSS) data. Limited by satellite positioning technology, further improving the accuracy of map matching (MM) is challenging work, especially for low-frequency trajectories. When matching a trajectory, the ego vehicle’s spatial-temporal information of the present trip is most useful with the least amount of data. In addition, there is a large number of other data, e.g., other vehicles’ state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths. Most of the MM studies have used only the ego vehicle’s data and ignored other vehicles’ data. Based on those, this article designs a new MM method to make full use of “big data.” We first sort all the data into four groups according to their spatial and temporal distance from the present matching probe, which allows us to sort for their usefulness. Then we design three different methods to extract valuable information (scores) from them: a score for speed and bearing, one for historical usage, and another for traffic state using a spectral graph Markov neural network. Finally, we use a modified top-K shortest-path method to search the candidate paths within an ellipse region and then use the fused score to infer the path (projected location). We test the proposed method against baseline algorithms using a real-world dataset in China. The results show that all scoring methods can enhance MM accuracy. Furthermore, our method outperforms the others, especially when the GNSS probing frequency is ≤ 0.01 Hz.

Inferring truck activities using privacy-preserving truck trajectories data

Global Navigation Satellite System (GNSS) data is an inexpensive and ubiquitous source of activity data. Global Positioning System (GPS) is an example of such data. Although there have been several studies about inferring device activity using GPS data from a consumer device, freight GPS data presents unique challenges for example having low and variable frequency, having long transmission gaps, and frequent and unpredictable device ID resetting for preserving privacy. This study aims to provide an end-to-end, generic data analytical framework to infer multiple aspects of truck activity such as stops, trips and tours. We use popular existing methods to construct the data processing pipeline and provide insights into their practical usage. We also propose improved data filters to different aspects of the data processing pipeline to address challenges found in privacy-preserving freight GPS data. We use freight data across four weeks from the greater Philadelphia region with variable transmission frequency ranging from one second to several hours to perform experiments and validate our methods. Our findings indicate that auxiliary information such as land use can be helpful in fine tuning stop inference,but spatio-temporal information contained in timestamped GPS pings is still the most powerful source of false stop identification. We also find that a combination of simple clustering techniques can provide a way to perform fast and reasonable clustering of the same stop.

Celestial frame tie from simulations using phase referencing to GNSS satellites

Aims.For decades now, researchers have been looking for a way to tie the kinematic and dynamic reference frames. Certain worldwide organizations have looked to using co-location in space, combining various techniques. Given the long list of possible applications of the Global Navigation Satellite System (GNSS), it is worthwhile investigating the connection between the most accurate and stable International Celestial Reference Frame (ICRF) and the Earth-centered Celestial Inertial reference frame (ECI) used in GNSS data processing.Methods.We simulated phase-referencing observations of GNSS satellites and nearby radio source calibrators to realize the connection between the two celestial reference frames. We designed two schemes for observation plans. One scheme is to select the satellite target when it can be observed by the greatest number of stations in order to obtain high-precision positioning. During each scan, we employ four regional networks to simultaneously track four chosen satellites. The alternative scheme is to observe satellite orbits of as many satellites as possible on different daily observations. In addition, to test the two schemes, we used Monte Carlo methods to generate 1000 groups of random errors in the simulation.Results.Finally, we estimate the right ascension and declination offsets (∆α, ∆δ) of GNSS satellites in the ICRF, and then derive frame tie parameters based on those results: three global rotation angles (A1,A2,A3). The celestial angular offset results assessed from the former scheme show that this scheme leads to high precision of namely 1 mas, while the parameters of the frame tie determined from the second scheme can achieve an improved precision of better than 1.3 µas.

Incorporation of Real-Time Earthquake Magnitudes Estimated via Peak Ground Displacement Scaling in the ShakeAlert Earthquake Early Warning System

ABSTRACTThe United States earthquake early warning (EEW) system, ShakeAlert®, currently employs two algorithms based on seismic data alone to characterize the earthquake source, reporting the weighted average of their magnitude estimates. Nonsaturating magnitude estimates derived in real time from Global Navigation Satellite System (GNSS) data using peak ground displacement (PGD) scaling relationships offer complementary information with the potential to improve EEW reliability for large earthquakes. We have adapted a method that estimates magnitude from PGD (Crowell et al., 2016) for possible production use by ShakeAlert. To evaluate the potential contribution of the modified algorithm, we installed it on the ShakeAlert development system for real-time operation and for retrospective analyses using a suite of GNSS data that we compiled. Because of the colored noise structure of typical real-time GNSS positions, observed PGD values drift over time periods relevant to EEW. To mitigate this effect, we implemented logic within the modified algorithm to control when it issues initial and updated PGD-derived magnitude estimates (MPGD), and to quantify MPGD uncertainty for use in combining it with estimates from other ShakeAlert algorithms running in parallel. Our analysis suggests that, with these strategies, spuriously large MPGD will seldom be incorporated in ShakeAlert’s magnitude estimate. Retrospective analysis of data from moderate-to-great earthquakes demonstrates that the modified algorithm can contribute to better magnitude estimates for Mw>7.0 events. GNSS station distribution throughout the ShakeAlert region limits how soon the modified algorithm can begin estimating magnitude in some locations. Furthermore, both the station density and the GNSS noise levels limit the minimum magnitude for which the modified algorithm is likely to contribute to the weighted average. This might be addressed by alternative GNSS processing strategies that reduce noise.

Assimilating GNSS PWV and radiosonde meteorological profiles to improve the PWV and rainfall forecasting performance from the Weather Research and Forecasting (WRF) model over the South China

Precipitable Water Vapor (PWV) and rainfall play important roles in the meteorological processes. In this study, we have investigated the impact of assimilation of Global Navigation Satellite System (GNSS) PWV and radiosonde profiles on the performance of Weather Research and Forecasting (WRF) model in forecasting PWV and rainfall over the South China region for April 01, 2020 to May 31, 2020. PWV observations derived from 213 GNSS stations and meteorological profiles recorded by 23 radiosonde stations are assimilated into WRF model. Five different WRF schemes are adopted: WRF scheme 0: no data assimilation (DA); WRF scheme 1: assimilation of PWV from 76 GNSS stations; WRF scheme 2: assimilation of PWV from 213 GNSS stations; WRF scheme 3: assimilation of meteorological profiles from 23 radiosonde stations; WRF scheme 4: assimilation of both PWV from 213 GNSS stations and meteorological profiles from 23 radiosonde stations. PWV observations derived from 170 independent GNSS (have not been used in assimilation) and rainfall data recorded by 648 surface meteorological stations are used to evaluate WRF forecasting performance in PWV and rainfall, respectively. The results indicate that all DA schemes improve the WRF forecasting performance for both PWV and rainfall. For the first 6 h after data assimilation, WRF schemes 1 to 4 improve the PWV forecasting accuracy by 11.6%, 14.5%, 2.9%, and 14.8%, respectively. For the accumulated rainfall within the first 6 h after data assimilation, WRF scheme 2 and WRF scheme 4 have a similar performance and outperform other DA schemes while the WRF scheme 4 is the best one among all five schemes. WRF scheme 4 improves the rainfall forecast probability of detection and equitable threat score by 0.085 and 0.057, respectively.

Data and Service Security of GNSS Sensors Integrated with Cryptographic Module.

Navigation and positioning are of increasing importance because they are becoming a new form of infrastructure. To ensure both development and security, this study designed a technical innovation structure to upgrade the GNSS (Global Navigation Satellite System) data transmission and real-time differential correction service system and proposed a new multiple cryptographic fusion algorithm to achieve the encryption and decryption of GNSS data and services. First, a GNSS station encrypts GNSS data with an encryption key and obtains a public key from a GNSS data center to encrypt the GNSS data encryption key. After that, identity authentication of a GNSS station is carried out, and an SSL VPN is established between the GNSS station and a GNSS data center before GNSS data are transmitted to the GNSS data center. Then, the GNSS data center decrypts the received GNSS data. The process of an intelligent terminal for real-time differential corrections is similar to that of the GNSS station and the GNSS data center. A GNSS sensor integrated with a cryptographic module was developed to validate the structure in an open environment. The results showed that the developed GNSS sensor was successful in encrypting the data, and the GNSS data center was able to decrypt the data correctly. For the performance test, a cryptography server was able support the requirements of GNSS applications. However, a cryptography server was optimal in supporting 40~50 GNSS stations simultaneously, whereas a cluster was suggested to be configured if the number of GNSS stations was more than 60. In conclusion, the method was able to ensure the validity, confidentiality, integrity, and non-repudiation of GNSS data and services. The proposed upgrading technology was suitable for coordinating GNSS development and security.

A Potential Earthquake with Magnitude Mw 7.2 on the Northern Xiaojiang Fault Revealed by GNSS Measurement

We used near-field and regional GNSS (Global Navigation Satellite System) data to quantify the deformation and locking ratio of the Xiaojiang fault (XJF) in southeastern Tibet. The inversion based on the dislocation model shows that the slip rate of the XJF is 9–11 mm/a; the locking depths of the northern, central, and southern segments are 25.5 km, 12 km, and 22.5 km, respectively. The inversion with DEFNODE program shows that the locking of the northern segment is the strongest above a depth of 20 km, while the locking between 20 km and 26 km is intermediate, and the weakest locking is found below 26 km. In the central segment, the depths of the interface are 6 km and 12 km. Additionally, a locked asperity that has the potential of generating an Mw 7.2 earthquake along the northern segment is delineated. The asperity and the shallow locking zone are basically consistent with the rupture area of the 1733 M 7.8 Dongchuan earthquake and the 1833 M 8 Songming earthquake, respectively. Both the activity of the historical strong earthquakes and the seismicity of the microearthquakes recorded over recent years seem to suggest that a potential earthquake is imminent.

QUALITY ASSESSMENTS OF UNMANNED AERIAL VEHICLE (UAV) AND TERRESTRIAL LASER SCANNING (TLS) METHODS IN ROAD CRACKS MAPPING

Abstract. In general, poor road conditions, specifically road cracks constitute a public nuisance, causing troublesome to road users, severe damage to vehicles and accidents. Hence, it is essential to detect the road crack earlier as an early-stage preventive for maintenance, but traditional inspection method used in Malaysia to physically collect the road information is extremely time-consuming, hazardous and labour-intensive. Nowadays, new technologies have improved the measuring performance. This leaves a gap in comparing two modern technologies in road cracks mapping. This study aims to explore the quality assessment between the unmanned aerial vehicle (UAV) and terrestrial laser scanning (TLS) methods in road cracks mapping. Two study areas inside the campus of Universiti Teknologi Malaysia (UTM), Johor, were selected. The ground control points (GCPs) and check points (CPs) at the study areas were georeferenced by global navigation satellite system (GNSS) data with MyRTKNet and ISKnet connections. Low altitude aerial imageries were collected using DJI Phantom 4 UAV, while Topcon GLS-2000 was employed to acquire the dense data of the cracked road. GNSS and manual inspection methods were performed to evaluate the results. In terms of mapping and measurement, both TLS and UAV methods produce almost similar results with average RMSE differences ranging from ±0.003 m to ±0.030 m and ±0.042 m to ±0.224 m respectively. This revealed that TLS is more accurate than UAV in mapping and measuring work. Differences in DTM quality across these approaches is below two (2) cm. Based on this study, TLS is more reliable than UAV. However, UAV offers advantages based on several considerations such as cost, time, safety, and accessibility. In summary, findings from this study shed some light to the authorities on the feasibility of UAV and TLS methods in road cracks mapping.

Pedestrian Augmented Reality Navigator.

Navigation is often regarded as one of the most-exciting use cases for Augmented Reality (AR). Current AR Head-Mounted Displays (HMDs) are rather bulky and cumbersome to use and, therefore, do not offer a satisfactory user experience for the mass market yet. However, the latest-generation smartphones offer AR capabilities out of the box, with sometimes even pre-installed apps. Apple's framework ARKit is available on iOS devices, free to use for developers. Android similarly features a counterpart, ARCore. Both systems work well for small spatially confined applications, but lack global positional awareness. This is a direct result of one limitation in current mobile technology. Global Navigation Satellite Systems (GNSSs) are relatively inaccurate and often cannot work indoors due to the restriction of the signal to penetrate through solid objects, such as walls. In this paper, we present the Pedestrian Augmented Reality Navigator (PAReNt) iOS app as a solution to this problem. The app implements a data fusion technique to increase accuracy in global positioning and showcases AR navigation as one use case for the improved data. ARKit provides data about the smartphone's motion, which is fused with GNSS data and a Bluetooth indoor positioning system via a Kalman Filter (KF). Four different KFs with different underlying models have been implemented and independently evaluated to find the best filter. The evaluation measures the app's accuracy against a ground truth under controlled circumstances. Two main testing methods were introduced and applied to determine which KF works best. Depending on the evaluation method, this novel approach improved the accuracy by 57% (when GPS and AR were used) or 32% (when Bluetooth and AR were used) over the raw sensor data.

The Design of GNSS/IMU Loosely-Coupled Integration Filter for Wearable EPTS of Football Players.

This study presents the filter design of GNSS/IMU integration for wearable EPTS (Electronic Performance and Tracking System) of football players. EPTS has been widely used in sports fields recently, and GNSS (Global Navigation Satellite System) and IMU (Inertial Measurement Unit) in wearable EPTS have been used to measure and provide players' athletic performance data. A sensor fusion technique can be used to provide high-quality analysis data of athletic performance. For this reason, the integration filter of GNSS data and IMU data is designed in this study. The loosely-coupled strategy is considered to integrate GNSS and IMU data considering the specification of the wearable EPTS product. Quaternion is used to estimate a player's attitude to avoid the gimbal lock singularity in this study. Experiment results validate the performance of the proposed GNSS/IMU loosely-coupled integration filter for wearable EPTS of football players.