Search Results (40)

This paper presents an angles-only navigation (AON) method utilizing optical observations of a single satellite with known ephemeris and prior altitude constraints given by an altimeter or known topography, which can enable near-ground platforms to achieve autonomous navigation in GNSS-denied environments. By leveraging a star tracker to measure the line-of-sight (LOS) direction of a satellite against a star background, the observer’s location is resolved via triangulation under geometric constraints. Theoretical error models are derived to analyze the influence of satellite position errors, LOS direction errors, and altitude uncertainties on geolocation accuracy. Numerical simulations validate the error propagation mechanisms, demonstrating that geolocation error is primarily determined by the perpendicular projection of orbital error relative to the LOS, increases linearly with LOS distance, and is sensitive to altitude errors at low elevation angles. Ground-based experiments conducted using Globalstar satellites achieve geolocation accuracy within 250 m (RMS), consistent with theoretical predictions. The proposed method offers a practical, low-cost solution for high-precision passive navigation in maritime and terrestrial applications. Full article

The Global Navigation Satellite System (GNSS) is extensively utilized in various applications that require triangulation solutions for positioning, navigation, and timing (PNT). These solutions are obtained by solving state estimates, traditionally using methods like weighted least squares (WLS) and Kalman Filters (KF). While these conventional approaches are foundational, they frequently encounter challenges related to robustness, particularly the necessity for precise noise statistics and the reliance on potentially accurate prior assumptions. This paper introduces a hybrid approach to GNSS state estimation, which integrates deep neural networks (DNNs) with the KF framework, employing the maximum likelihood principle for unsupervised training. Our methodology combines the strengths of DNNs with conventional KF techniques, leveraging established model-based priors while enabling flexible, data-driven modifications. We parameterize components of the Extended Kalman Filter (EKF) using neural networks, training them with a probabilistically informed maximum likelihood loss function and backpropagation. We demonstrate that this hybrid method outperforms classical algorithms both in terms of accuracy and flexibility for easier implementation, showing better than 30% improvement in the variance of the horizontal position for the simulated as well as the real-world dynamic receiver dataset. For the real-world dynamic dataset, our method also provides a better fit to the measurements than the classical algorithms. Full article

Flash floods are highly destructive natural disasters, particularly in arid and semi-arid regions like Egypt, where data scarcity poses significant challenges for analysis. This study focuses on the Wadi Al-Barud basin in Egypt’s Central Eastern Desert (CED), where a severe flash flood occurred on 26–27 October 2016. This flash flood event, characterized by moderate rainfall (16.4 mm/day) and a total volume of 8.85 × 10⁶ m³, caused minor infrastructure damage, with 78.4% of the rainfall occurring within 6 h. A significant portion of floodwaters was stored in dam reservoirs, reducing downstream impacts. Multi-source data, including Landsat 8 OLI imagery, ALOS-PALSAR radar data, Global Precipitation Measurements—Integrated Multi-satellite Retrievals for Final Run (GPM-FR) precipitation data, geologic maps, field measurements, and Triangulated Irregular Networks (TINs), were integrated to analyze the flash flood event. The Soil Conservation Service Curve Number (SCS-CN) method integrated with several hydrologic models, including the Hydrologic Modelling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), and European Hydrological System Model (MIKE-SHE), was applied to evaluate flood forecasting, watershed management, and runoff estimation, with results cross-validated using TIN-derived DEMs, field measurements, and Landsat 8 imagery. The SCS-CN method proved effective, with percentage differences of 5.4% and 11.7% for reservoirs 1 and 3, respectively. High-resolution GPM-FR rainfall data and ALOS-derived soil texture mapping were particularly valuable for flash flood analysis in data-scarce regions. The study concluded that the existing protection plan is sufficient for 25- and 50-year return periods but inadequate for 100-year events, especially under climate change. Recommendations include constructing additional reservoirs (0.25 × 10⁶ m³ and 1 × 10⁶ m³) along Wadi Kahlah and Al-Barud Delta, reinforcing the Safaga–Qena highway, and building protective barriers to divert floodwaters. The methodology is applicable to similar flash flood events globally, and advancements in geomatics and datasets will enhance future flood prediction and management. Full article

This study introduces a novel method that addresses the challenge of visualizing and quantifying detection coverage areas in wireless sensor networks. The method involves projecting a network of range-free sensors and pre-existing transmitters, located within a predefined area of interest, onto a global coordinate system. Detection areas are defined as those covered by the sensing range of at least three sensors. Pre-existing transmitters located within the detection range of the sensors are assumed to degrade the networks’ performance by causing coverage gaps. Interactive satellite maps facilitate the dynamic exploration of coverage via the calculation and visualization of the resulting detection areas. The algorithmic structure of the proposed tool is explained in detail, and four example scenarios demonstrate the tool’s capabilities, as well as its flexibility, adaptability, and effectiveness in identifying the triangulated detection areas. Designed primarily as a geometry calculation and visualization tool that allows for the adjustment of sensor parameters such as locations, ranges, and angular ranges of detection, the proposed tool has the potential to enhance decision-making in sensor network configuration, prior to final sensor placement, across a wide range of applications. Full article

With the enrichment of GNSS data and the improvement in data processing accuracy, GNSS technology has been widely applied in fields such as crustal deformation. The Crustal Movement Observation Network of China (CMONOC) has provided decades of Global Navigation Satellite System (GNSS) data and related data products for crustal deformation research on the Chinese mainland. The coordinate time series of continuously observed reference stations contain abundant information on crustal movements. In contrast, the coordinate time series of periodically observed campaign stations have limited data, making it difficult to separate or remove instantaneous non-tectonic movements from the time series, as performed with reference stations, to obtain a stable and reliable crustal movement velocity field. To address this issue, this paper proposes a method to improve the estimation of crustal movement velocity at campaign stations using the information of neighboring reference stations. This method constructs a Delaunay triangulation of reference stations and fits the periodic movement of each campaign station using an inverse distance weighted interpolation algorithm based on the reference station information. The crustal movement velocity of the campaign stations is then estimated after removing the periodic movement. This method was verified by its application to the estimation of the vertical motion rate at some reference and campaign stations in Yunnan Province. The results show that the accuracy of vertical motion rate estimation for virtual and real campaign stations improved by an average of 24.4% and 9.6%, respectively, demonstrating the effectiveness of the improved method, which can be applied to estimate crustal movement velocity at campaign stations in other areas. Full article

This research investigates precision enhancement in regional quasi-geoid refinement through ADS80 tri-linear array scanning stereoscopic imagery for aerial triangulation coupled with GNSS gravity-potential modeling. By acquiring stereoscopic imagery and analyzing triangulation accuracy using an ADS80 camera, we performed this study over the Qinghai–Tibet Plateau’s elevated, desolate terrain, collecting 593 GNSS points following high-precision stereoscopic imagery modeling. By utilizing 12 gravity satellite models, we computed geoid heights and China’s 1985 Yellow Sea elevations for 28 benchmarks and GNSS points, thereby refining the Qinghai Province Quasi-Geoid Model (QPQM) using geometric techniques. The findings reveal that POS-assisted ADS80 stereoscopic imagery yields high-precision triangulation with maximal horizontal and elevation accuracies of 0.083/0.116 cm and 0.053/0.09 cm, respectively, across five control point arrangements. The RMSE of normal heights for 1985, processed via these GNSS points, achieved decimeter precision. By applying error corrections from benchmarks to the 1985 elevation data from gravity satellites and performing weighted averaging, the precision of EGM2008, SGG-UGM-2, and SGG-UGM-1 models improved to 8.61 cm, 9.09 cm, and 9.38 cm, respectively, surpassing the QPQM by 9.22 cm to 9.99 cm. This research demonstrates that the proposed methods can significantly enhance the precision of regional quasi-geoid surfaces. Additionally, these methods offer a novel approach for rapidly establishing regional quasi-geoid models in the uninhabited areas of the Qinghai–Tibet Plateau. Full article

This paper presents the description of antenna parameters related to its radiation/reception capabilities influenced by the plasma parameters in the environment surrounding the antenna, complementing the existing works on the antenna parameters (e.g., the impedance or currents). The parameters considered are the radiation zones’ radiuses (inductive, Fresnel, Fraunhofer), scalloping and directivity; a method of transformation of the air/vacuum-measured radiation/reception pattern to the pattern expected for given plasmatic conditions is also considered. Three different simplified plasma conditions are taken into account (different electron densities: 1.4 × 10¹² m⁻³, 4 × 10¹¹ m⁻³ and 10⁸ m⁻³), with varying antenna length (1 m, 10 m, 100 m) and signal propagation mode (classic-ionospheric, whistler and Alfvén). The findings show that the presented antenna parameters and its radiation/reception pattern are heavily dependent on the plasma conditions. These findings can be used to form additional requirements and constraints for the mechanical design of new instrumentation for space weather measurements on board spacecraft (e.g., moving the antennas away from the spacecraft in order not to alter their radiation/reception patterns or not to measure the plasma around the spacecraft) or more accurate data processing from existing space weather satellites, allowing, for example, a more precise triangulation of the signal source or its spectral power regarding the actual performance of the antennas submerged in plasma. Full article

Jakarta holds the distinction of being the largest capital city among ASEAN countries and ranks as the second-largest metropolitan area in the world, following Tokyo. Despite numerous studies examining the diverse urban land use and land cover patterns within the city, the recent state of urban green spaces has not been adequately assessed and mapped precisely. Most previous studies have primarily focused on urban built-up areas and manmade structures. In this research, the first-ever detailed map of Jakarta’s urban green spaces as of 2023 was generated, with a resolution of three meters. This study employed a combination of supervised classification and evaluated two machine learning algorithms to achieve the highest accuracy possible. To achieve this, various satellite images were utilized, including VV and VH polarizations from Sentinel-1, multiple bands from Sentinel-2, and eight bands from Planet. The Planet data were subsequently transformed into the Red-Edge Triangulated Vegetation Index and Red-Edge Triangulated Wetness Index. The data training and testing samples for urban green spaces were obtained using the Street View images available on Google Maps. The results revealed that using the Random Forest classifier algorithm and only eight bands of Planet images achieved an accuracy rate of 84.9%, while a combination of multiple images achieved an impressive 95.9% accuracy rate. Jakarta’s urban areas cover approximately 33.2% of green spaces. This study provides unprecedented insights into the type, size, and spatial distribution of Jakarta’s urban green spaces, enabling urban residents and stakeholders to explore and promote healthier living and better manage these green areas. Additionally, a previously unexplored concept, Jakarta’s urban green belt, is introduced. Full article

The performance of three-dimensional (3D) point cloud reconstruction is affected by dynamic features such as vegetation. Vegetation can be detected by near-infrared (NIR)-based indices; however, the sensors providing multispectral data are resource intensive. To address this issue, this study proposes a two-stage framework to firstly improve the performance of the 3D point cloud generation of buildings with a two-view SfM algorithm, and secondly, reduce noise caused by vegetation. The proposed framework can also overcome the lack of near-infrared data when identifying vegetation areas for reducing interferences in the SfM process. The first stage includes cross-sensor training, model selection and the evaluation of image-to-image RGB to color infrared (CIR) translation with Generative Adversarial Networks (GANs). The second stage includes feature detection with multiple feature detector operators, feature removal with respect to the NDVI-based vegetation classification, masking, matching, pose estimation and triangulation to generate sparse 3D point clouds. The materials utilized in both stages are a publicly available RGB-NIR dataset, and satellite and UAV imagery. The experimental results indicate that the cross-sensor and category-wise validation achieves an accuracy of 0.9466 and 0.9024, with a kappa coefficient of 0.8932 and 0.9110, respectively. The histogram-based evaluation demonstrates that the predicted NIR band is consistent with the original NIR data of the satellite test dataset. Finally, the test on the UAV RGB and artificially generated NIR with a segmentation-driven two-view SfM proves that the proposed framework can effectively translate RGB to CIR for NDVI calculation. Further, the artificially generated NDVI is able to segment and classify vegetation. As a result, the generated point cloud is less noisy, and the 3D model is enhanced. Full article

The CartoDEM Version 3 Release 1 openly accessible datasets are currently the most reliable datasets for relatively plain regions in India specifically. The aim of the presented study is to evaluate CartoDEM with respect to two openly accessible spaceborne LiDAR datasets from two LiDAR sensors: the Advanced Topographic Laser Altimeter System (ATLAS) on board the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI) over the International Space Station (ISS). The differences and deviations were computed for CartoDEM and LiDAR footprint elevations for the two datasets, namely, ICESat-2 and GEDI. The difference values were filtered for footprints with differences between 0 and 2.5 in the DEM and LiDAR elevation values. Besides this, an overall estimate was also calculated for the elevation values obtained over the surface, i.e., the ground, as well as objects such as the trees or buildings. The RMSEs were observed to be 1.16 m and 1.74 m for the ICESat-2 and GEDI datasets for the points/footprints on the terrain, whereas when considering similar parameters for the two datasets, the RMSEs were found to be 1.78 m and 5.48 m for the ICESat-2 and GEDI footprints on the surface (terrain/object), respectively. This study reveals that CartoDEM is highly accurate in the plain regions when validated with respect to the ICESat-2 datasets, which work via the photon counting technique. Further, it was observed that ICESat-2’s performance is better than that of the GEDI mission for terrain height. Thus, it was observed that the spaceborne LiDAR datasets from ICESat-2 can be utilized for the validation of DEMs and can be useful for applications where an input to a DEM is required for engineering or modeling applications. Full article

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day. Full article

One of the important issues being explored in Industry 4.0 is collaborative mobile robots. This collaboration requires precise navigation systems, especially indoor navigation systems where GNSS (Global Navigation Satellite System) cannot be used. To enable the precise localization of robots, different variations of navigation systems are being developed, mainly based on trilateration and triangulation methods. Triangulation systems are distinguished by the fact that they allow for the precise determination of an object’s orientation, which is important for mobile robots. An important feature of positioning systems is the frequency of position updates based on measurements. For most systems, it is 10–20 Hz. In our work, we propose a high-speed 50 Hz positioning system based on the triangulation method with infrared transmitters and receivers. In addition, our system is completely static, i.e., it has no moving/rotating measurement sensors, which makes it more resistant to disturbances (caused by vibrations, wear and tear of components, etc.). In this paper, we describe the principle of the system as well as its design. Finally, we present tests of the built system, which show a beacon bearing accuracy of $\Delta \phi$ = 0.51$°$, which corresponds to a positioning accuracy of $\Delta R$ = 6.55 cm, with a position update frequency of $f_{update}$ = 50 Hz. Full article

The measurement of bidirectional reflectivity for ground-based objects is a highly intricate task, with significant limitations in the capabilities of both ground-based and satellite-based observations from multiple viewpoints. In recent years, unmanned aerial vehicles (UAVs) have emerged as a novel remote sensing method, offering convenience and cost-effectiveness while enabling multi-view observations. This study devised a polygonal flight path along the hemisphere to achieve bidirectional reflectance distribution function (BRDF) measurements for large zenith angles and all azimuth angles. By employing photogrammetry’s principle of aerial triangulation, accurate observation angles were restored, and the geometric structure of “sun-object-view” was constructed. Furthermore, three BRDF models (M_Walthall, RPV, RTLSR) were compared and evaluated at the UAV scale in terms of fitting quality, shape structure, and reflectance errors to assess their inversion performance. The results demonstrated that the RPV model exhibited superior inversion performance followed, by M_Walthall; however, RTLST performed comparatively poorly. Notably, the M_Walthall model excelled in capturing smooth terrain object characteristics while RPV proved applicable to various types of rough terrain objects with multi-scale applicability for both UAVs and satellites. These methods and findings are crucial for an extensive exploration into the bidirectional reflectivity properties of ground-based objects, and provide an essential technical procedure for studying various ground-based objects’ in-plane reflection properties. Full article

We developed the first-ever bathymetric module for the NASA Ames Stereo Pipeline (ASP) open-source topographic software called Satellite Triangulated Sea Depth, or SaTSeaD, to derive nearshore bathymetry from stereo imagery. Correct bathymetry measurements depend on water surface elevation, and whereas previous methods considered the water surface horizontal, our bathymetric module accounts for the curvature of the Earth in the imagery. The process is semiautomatic, reliable, and repeatable, independent of any external bathymetry data eliminating user bias in selecting bathymetry calibration points, and it can generate a fully integrated and seamless topo-bathymetry digital elevation model (TBDEM) in the same coordinate system, comparable with the band-ratio method irrespective of the regression method used for the band-ratio algorithm. The ASP output can be improved by applying a camera bundle adjustment to minimize reprojection errors and by alignment to a more accurate topographic (above water) surface without any bathymetric input since the derived TBDEM is a rigid surface. These procedures can decrease bathymetry root mean square errors from 30 to 80 percent, depending on environmental conditions, the quality of satellite imagery, and the spectral band used (e.g., blue, green, or panchromatic). Full article

Conjunction observations of auroras with electron distributions and broadband electrostatic fluctuations on Van Allen Probe A satellite in the equatorial region are considered. Using triangulation measurements, the energy spectra of the precipitating electrons in the rayed auroral structures were determined for the 17 March 2015 event. A comparison of the spectra of precipitating electrons in the auroral rays with satellite measurements of electrons in the equatorial region related to the aurora showed their agreement. The concomitance between Van Allen Probe A broadband electric waves and auroral variations measured by the ground-based auroral camera was observed on 17 March 2015. This suggests that broadband electrostatic waves may be responsible for electron precipitation, leading to the formation of rayed structures in the aurora. Full article