Volume 18, Issue 4

Freeform surfaces like tensor product B-spline surfaces have been proven to be a suitable tool to model laser scanner point clouds, especially those representing artificial objects. However, when it comes to the modelling of point clouds representing natural surfaces with a lot of local structures, tensor product B-spline surfaces reach their limits. Refinement strategies are usually used as an alternative, but their functional description is no longer nearly as compact as that of classical tensor product B-spline surfaces, making subsequent analysis steps considerably more cumbersome. In this publication, the approximation quality of classical tensor product B-spline surfaces is improved by means of local parameterization. By using base surfaces with a local character, relevant information about local structures of the surface to be estimated are stored in the surface parameters during the parameterization step. As a consequence, the resulting tensor product B-spline surface is able to represent these structures even with only a small number of control points. The developed locally parameterized B-spline surfaces are used to model four data sets with different characteristics. The results reveal a clear improvement compared to the classical tensor product B-spline surfaces in terms of correctness, goodness-of-fit and stability.

The development of multi-sensor systems (MSSs) goes hand in hand with assessing the quality of these complex systems. Therefore, reliable reference information of superior accuracy is essential for validation, serving as ground truth. When the assessment is based on 3D point cloud comparison, appropriate reference environments with suitable geometries are required. However, validation of an MSS can also be performed directly on its 3D position or even its 6D pose. This is particularly suitable for systems without sensors for environmental acquisition. When using kinematic measurement systems, the temporal relationship between the MSS and the reference trajectory must be considered, which can be challenging. Modern sensors of an MSS are often synchronized to GPS time. However, this global time information is not provided for high-accuracy tracking sensors such as robotic total stations (RTSs) or laser trackers (LTs). Instead, only a reference to an internal sensor time is implemented, which does not meet the highest quality requirements for reference trajectories. Depending on the motion speed, time offsets in the millisecond range can lead to significant trajectory inaccuracies. This paper presents investigations for GPS time synchronization of the polar measurement elements of both RTS and LT from Leica Geosystems. While the LT uses a precise trigger signal for the time link, the RTS requires the Measure & Stream application provided by the instrument manufacturer. The two different approaches are presented theoretically. Based on empirical long-term studies, their possibilities and limitations are critically discussed. For the high-frequency LT, reference trajectories with precise synchronization based on GPS time can be reliably realized. With an RTS, improved synchronization can be achieved by using Measure & Stream. Thus, 43 % lower tangential deviations from the reference could be achieved compared to the raw data. However, there are still inconstant time offsets of 63.9 ms on average. Depending on the speed of the target, this can lead to significant position deviations. Further investigations are required. In general, the developed approaches for the realization of GPS-synchronized reference trajectories can be used not only for the quality assessment of MSS, but also for monitoring or kinematic positioning applications.

Terrestrial laser scanners (TLSs) have become indispensable for acquiring highly detailed and accurate 3D representations of the physical world. However, the acquired data is subject to systematic deviations in distance measurements due to external influences, such as distance and incidence angle. This research introduces a calibration approach by applying a deep learning model based on PointNet to predict and correct these systematic distance deviations, incorporating not only the XYZ coordinates but also additional features like intensity, incidence angle, and distances within a local neighbourhood radius of 5 cm. By predicting and subsequently correcting systematic distance deviations, the quality of TLS point clouds can be improved. Hence, our model is designed to complement and build upon the foundation of prior internal TLS calibration. A data set collected under controlled environmental conditions, containing various objects of different materials, served as the basis for training and validation the PointNet based model. In addition our analysis showcase the model’s capability to accurately model systematic distance deviations, outperforming existing methods like gradient boosting trees by capturing the spatial relationships and dependencies within the data more effectively. By defining test data sets, excluded from the training process, we underscore the ongoing effectiveness of our model’s distance measurement calibration, showcasing its ability to improve the accuracy of the TLS point cloud.

Kinematic multi-sensor systems (MSS) describe their movements through six-degree-of-freedom trajectories, which are often evaluated primarily for accuracy. However, understanding their self-reported uncertainty is crucial, especially when operating in diverse environments like urban, industrial, or natural settings. This is important, so the following algorithms can provide correct and safe decisions, i.e. for autonomous driving. In the context of localization, light detection and ranging sensors (LiDARs) are widely applied for tasks such as generating, updating, and integrating information from maps supporting other sensors to estimate trajectories. However, popular low-cost LiDARs deviate from other geodetic sensors in their uncertainty modeling. This paper therefore demonstrates the uncertainty evaluation of a LiDAR-based MSS localizing itself using an inertial measurement unit (IMU) and matching LiDAR observations to a known map. The necessary steps for accomplishing the sensor data fusion in a novel Error State Kalman filter (ESKF) will be presented considering the influences of the sensor uncertainties and their combination. The results provide new insights into the impact of random and systematic deviations resulting from parameters and their uncertainties established in prior calibrations. The evaluation is done using the Mahalanobis distance to consider the deviations of the trajectory from the ground truth weighted by the self-reported uncertainty, and to evaluate the consistency in hypothesis testing. The evaluation is performed using a real data set obtained from an MSS consisting of a tactical grade IMU and a Velodyne Puck in combination with reference data by a Laser Tracker in a laboratory environment. The data set consists of measurements for calibrations and multiple kinematic experiments. In the first step, the data set is simulated based on the Laser Tracker measurements to provide a baseline for the results under assumed perfect corrections. In comparison, the results using a more realistic simulated data set and the real IMU and LiDAR measurements provide deviations about a factor of five higher leading to an inconsistent estimation. The results offer insights into the open challenges related to the assumptions for integrating low-cost LiDARs in MSSs.

The Mw 6.2 Mamuju-Majene earthquake occurred on 14 January 2021, with the epicenter at 118.890°E, 2.972°S. The shaking caused severe damage in West Sulawesi, especially in the Mamuju and Majene cities. Most of the coseismic slip distribution of the Mamuju-Majene Earthquake is derived from the daily solutions, which might include early postseismic deformation. Therefore, we conducted a coseismic slip model using kinematic solution based on Global Navigation Satellite System (GNSS) to determine the best coseismic slip values and model distribution. Our analysis indicates that the coseismic displacement from the kinematic solution is higher than the static solution. The GNSS data was utilized for inversion analysis, considering two potential fault sources, they are the Makassar Strait Central Fault and the Mamuju Fault. We found a larger misfit between the observed data and the model generated on static and kinematic solutions along the Makassar Strait Central Fault. Based on the kinematic solution, the coseismic slip distribution represents that fault rupture spreading along a north-south orientation, while the static solution is centered in the northern part. The maximum coseismic slip from each kinematic and static solution is 0.29 m and 0.11 m, respectively. Meanwhile, the seismic moment generated from the kinematic solution is 1.5 × 10 26  N m (equivalent to Mw 6.75), which is greater than the static solution of 2.4 × 10 25  N m (equivalent to Mw 6.22).

The Operational Navigation with Indian Constellation (NavIC) comprises seven satellites in the orbit, including three in geostationary orbit (GEO) and four in geosynchronous orbit (GSO). NavIC provides both Standard Positioning Service and Restricted Service, using L5 (1176.45 MHz) and S1 (2492.028 MHz) frequencies, with coverage extending 1500 km around the mainland of India. In an urban canyon, multipath interference severely reduces the precision and reliability of NavIC positioning. Many current multipath mitigation techniques often exhibit high computational requirements or reliance on external assistance. In this paper, a ranging code tracking loop is proposed that can sustain either a late or early branch in contrast to the Narrow-Spacing (NS) correlation technique for mitigating multipath for NavIC receiver. The design of proposed code tracking loop is based on steepest descent algorithm. The findings demonstrate that, in terms of calculation time and code multipath mitigation, the suggested technique performs better than both Multipath Estimated Delay Locked Loop (MEDL) and NS correlation. The proposed method produces less than 0.016 chips for the tracking error Standard Deviation (STD). In addition, the recommended method takes 24 % less computation time.

The modeling and forecasting of Total Electron Content (TEC) play a major role in influencing signals from satellite-based navigation systems and impact the performance of diverse satellite-dependent technologies. The intensity of solar ionizing radiation and the state of geomagnetic field activity influence the Global Navigation Satellite System (GNSS)-TEC. This paper uses a Linear TEC Function (LTF) climatology model to understand ionospheric behavior under solar and geomagnetic activities that cause variations in the electron distribution of the ionosphere medium. The LTF model integrates representations of solar EUV photon (MgII) and geomagnetic (SYMH) activities, incorporating solar-modulated oscillations (periodic variations) at four seasonal cycles and a linear trend. The LTF model examined the time series of GPS-TEC at a location (geographic 34.95° N, 134.05° E) with a time resolution of 1 h, from 1997 to 2016, covering solar cycles 23 and 24. The Root Mean Square Deviation (RMSD) and correlation coefficient between the GNSS-TEC and model TEC (LTF) was 5.30 TECU and 95 %. The results indicate that solar components, as well as annual and semi-annual variations, have a significant impact on the daily average TEC. Solar activity appears to be the predominant determining factor of TEC during the solar phases of cycles 23 and 24. In contrast, periodic influences primarily outline TEC during periods characterized by minimal solar activity. The geomagnetic component presents an increased influence, particularly during storm periods. The model demonstrates superior performance in Total TEC modeling compared to other state-of-the-art approaches.

Detecting and identifying outliers/failures in GNSS measurements has garnered significant attention among researchers aiming to enhance the quality of GNSS positioning and navigation. This study delves into the analysis of the separability of multiple outliers when single, double, and triple outliers occur in single-point positioning (SPP) measurements. To achieve this, a novel method includes introducing a multi-dimensional correlation coefficient among test statistics. This coefficient functions as a measure of outliers separability and, in turn, assesses the possible impact of outliers on other measurements. This multi-dimensional correlation approach is based on a nested correlation ( ρ nested θ , φ ${\rho }_{\text{nested}}^{\theta ,\varphi }$ ) that explains the variations in test statistic values with and without common measurements in two pairs/combinations. The performance of the ρ nested θ , φ ${\rho }_{\text{nested}}^{\theta ,\varphi }$ is then compared with other two existing methods of multi-dimensional correlation namely the maximum ( ρ max θ , φ ${\rho }_{\mathrm{max}}^{\theta ,\varphi }$ ) and global ( ρ Global θ , φ ${\rho }_{\text{Global}}^{\theta ,\varphi }$ ) correlation. The results show that under the presence of two outliers and with and without common measurements in two pairs, the ρ nested θ ${\rho }_{\text{nested}}^{\theta }$ outperforms the, ρ max θ ${\rho }_{\mathrm{max}}^{\theta }$ and ρ Global θ ${\rho }_{\text{Global}}^{\theta }$ exhibiting a determination coefficient ( R 2 ) value of approximately 0.95 and 0.62 respectively. The results furthermore reveal that for three outliers and with one, two, and noncommon measurements intersecting between two combinations, the values of R 2 are 0.62, 0.96, and 0.34. respectively. This means that the ρ nested θ , φ ${\rho }_{\text{nested}}^{\theta ,\varphi }$ can explain the variations in outlier test statistic values particularly in the case that common measurements appear in two pairs/combinations.

Global Navigation Satellite Systems (GNSS) have become an integral part of modern life, supporting various applications, from precise positioning and navigation to timing and synchronization. However, GNSS signals are vulnerable to natural interferences including various atmospheric disturbances, with ionospheric scintillations being a significant challenge. Ionospheric scintillations, caused by irregularities in the Earth’s ionosphere, introduce rapid fluctuations in the amplitude and phase of GNSS signals. These fluctuations can severely degrade the accuracy and reliability of GNSS receivers, leading to positioning errors and navigation failures. Hence, it is crucial to develop effective mitigation strategies. One of the promising approaches to mitigate ionospheric scintillations is the utilization of Software Defined Radio (SDR) technology in GNSS receivers. SDR allows for real-time adaptation to changing signal conditions, enabling the receiver to detect scintillations and adjust its signal processing accordingly. This adaptability enhances the receiver’s stability against ionospheric disturbances, ensuring more robust and accurate positioning and navigation. In this paper, preliminary results of GNSS SDR (Make: iP-Solutions, Japan) installed at Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram (Guntur) (16.44° N, 80.62° E) are presented. Amplitude scintillation index (S4) variations for different PRNs and subsequent positioning results are interpreted from April to September 2023. The results are compared and validated with those of the co-located Novatel GNSS receiver and NAVIC receiver. Most of the S4 variations correlate well with the S4 values from the Novatel and NAVIC receivers. S4 observations from the Septentrio receiver at Daytona Beach (Florida) are also presented. The results of SDR will be extended further for the development of scintillation mitigation algorithms. We plan to install an SDR and employ similar mitigation strategy at this location in the near future.

Multiple constellations and their combinations in the global navigation satellite systems (GNSS) provide a great opportunity for single-station precise point positioning (PPP) models. The PPP models are of more importance for GNSS users as they are cost-effective with reasonable accuracy. There are abundant models in the market that use different data processing techniques based on the location and constellations used. In this study, we used the precise point positioning (PPP) software Net_Diff to verify the positioning accuracy at a low latitude Indian location using individual global satellite constellations (GPS, GLONASS, Galileo, BeiDou) and their combinations with GPS fixed. The ionospheric correction models such as GIM and Klobuchar are applied in the post-processing to determine the positioning accuracy. The Kalman filter method is applied to model the input data along with including the noise to derive the position solution. The results revealed that the GPS showed constant residual error for both quiet and disturbed days. In terms of single constellation Galileo produced less residuals in WGS 84 and ECEF coordinate systems whereas in the multi-constellation combination GPS, Galileo and GLONASS resulted in residuals of lesser magnitudes compared to the other combinations considered in this study. The RMS and STD of the residuals confirmed the high precision for Galileo and low precision for BeiDou constellations at the location.

In order to study the coupling relationship between earthquakes and ionospheric disturbances, TEC data during the Mw 7.3 earthquake that occurred near Fukushima, Japan on March 16, 2022, were processed using global ionospheric data provided by the Center for Orbit Determination in Europe (CODE). In this paper, the sliding quartile interval method is used to eliminate solar activity in a 27-day window, including sunspot number (SSN), F10.7 cm radio flux (F10.7), total solar irradiation (TSI), solar wind velocity (Vsw) and geomagnetic activity. The impact of disturbance storm time index (DST) and global geomagnetic activity index (KP) on TEC anomaly disturbance can obtain more accurate TEC anomaly information. The results indicate that when the solar and geomagnetic activity cycles are inconsistent with the TEC anomalies on fifth day before the earthquake, the TEC anomalies above the epicenter are significantly greater than those observed in other regions, and the corresponding magnetic conjugate region is accompanied by anomalies, which is the characteristic of TEC anomalies caused by earthquakes. This means that the detected TEC anomalies can be used as a potential ionospheric precursors, indicating that the Fukushima earthquake is imminent.

The technological advancements in the current era have highlighted the increasing significance of satellite-based positioning, navigation, and timing services in a wide range of dynamic and critical applications. This has led to significant efforts towards enhancing the performance of global navigation satellite systems (GNSS) operating under challenging ionospheric conditions. The Sri Lankan ionosphere region is a focal point of equatorial aeronomy scientists, being situated in the southernmost landmass of the Indian longitude sector within the vicinity of the magnetic equator where a combination of electric, wind, and temperature dynamics exerts a substantial influence on the ionosphere but was relatively unexplored in the past. In the present work, we employed a Kriging interpolation technique on the total electron content (TEC) variables from ten GNSS receivers operating under the Continuously Operating Reference Stations (CORS) network in Sri Lanka first ever of its kind to deliver two-dimensional regional ionospheric TEC maps at hourly intervals, both during quiet and disturbed ionospheric conditions in the equinoctial March and April months of 2022. The latitudinal variation patterns are discernable from the hourly TEC maps. Furthermore, a comparative analysis of the performance of GNSS-derived TEC with that of the routinely published Global Ionospheric Maps (GIMs) confirms overestimation characteristics of the latter irrespective of the local time of observation. The generated regional ionospheric maps are fairly responsive to the onset of the storm and the recovery phase thereafter. The extent of nighttime ionospheric irregularity is also probed through the rate of TEC index (ROTI) variations, demonstrating that the irregularities were insignificant during the selected storm event.