Visible Satellites Research Articles

Determination of the Minimum Safe Distance between a USV and a Hydro-Engineering Structure in a Restricted Water Region Sounding

Bathymetric surveys performed using small, unmanned vessels are increasingly used in coastal areas and regions difficult to access by hydrographic motorboats. Their geometric dimensions, manoeuvring parameters, low labour intensity, and costs of survey execution have allowed the unmanned survey vessel (USV) to be a commonly recognised surveying platform. It is equipped with a navigation system for positioning, maintaining a course or survey line, determining spatial orientation, and measuring depths. The operation zone of the global navigation satellite system (GNSS) in coastal water regions enables geodetic positioning in land-based surveys and of moving objects, also including, for example, a sounding vessel. Under difficult observational conditions, the positioning is limited by the obscuration of the upper hemisphere, i.e., the visibility of satellites and the reflection from high field buildings. This poses a threat to a small vessel operating at a very short distance from a hydro-engineering structure. Based on a study performed in a marina, the article presents the determination of the minimum safe distance of the planned survey line to the quay in terms of the USV’s dimensions under good sounding conditions. These include low and constant velocity and good observational conditions for a GNSS receiver. The analysis was conducted on survey lines perpendicular to the quay, which was approached twice at distances of 1–5 m, with a 0.5 m interval. A 1 m distance between the end of the survey line and the quay has been determined for the safety of USV’s navigation and continuity of geospatial data collection during bathymetric surveys.

Multi-antenna GNSS tight combination attitude determination in the urban environment

It is a challenging task to determine dynamic vehicle attitude using a single-frequency single-epoch multi-antenna global navigation satellite system (GNSS). In the urban environment, the number of visible satellites drops sharply due to the occlusion of trees and tall buildings, hence it is difficult to obtain the high-precision attitude of vehicles using only a multi-antenna GNSS. The GNSS tight combination algorithm selects the same reference satellite between different systems, and can effectively increase the number of observation equations after eliminating the inter-system bias, to improve the attitude accuracy of vehicles in complex environments. Compared with the loose combination algorithm, which selects reference satellites separately between different systems, the tight combination algorithm can further improve the locatable performance when there are fewer satellites. Dynamic vehicle experiments were carried out in an open environment and a complex environment, respectively, using the GPS/BDS/GALILEO three-system single-frequency and single-epoch positioning mode. The results show that the tight combination algorithm and the loose combination algorithm have the same accuracy when there are enough visible satellites in the open environment. In a complex environment, with a cut-off elevation angle of 40°, the percentage of pitch angle error, yaw angle error and roll angle error within 2° increased by 6.1%, 8.07% and 13.43%, respectively, and the ambiguity fixed rate was increased by 14.78%.

Development and evaluation of a generalized model of RAIM availability for single-, dual- and multi-satellite faults

Receiver autonomous integrity monitoring (RAIM) fault monitoring and identification requires first to judge the geometric distribution of currently visible satellites based on performance indicators and to determine whether it is suitable for integrity monitoring, that is, to judge the availability of the RAIM algorithm. With the rapid development of the global navigation satellite system (GNSS), the possibility of dual-satellite faults and multiple-satellite faults in GNSS navigation and positioning has greatly increased. The existing literature mainly focuses on RAIM availability evaluation for single-satellite fault or dual-satellite faults, ignoring the occurrence possibility of multiple-satellite faults, and thus there is no unified RAIM availability evaluation model. To this end, we use the characters of the quadratic matrix to derive a generalized model of RAIM availability evaluation, and the model is suitable for situations of single-, dual- and multi-satellite faults. Based on the observation data of Beidou Navigation Satellite System (BDS) from 332 International GNSS Service (IGS) stations distributed globally on 24 March 2021 and the global ephemeris file, the global RAIM availability of BDS is evaluated. The results show that the generalized model of RAIM availability evaluation has a rigorous derivation process, strong applicability and easy implementation. When the cutoff elevation angle is greater than 5°, the average number of BDS visible satellites at 332 IGS stations worldwide is 12.289 and the average geometry dilution of precision value is 8.090. This can fully meet RAIM availability evaluation and integrity monitoring requirements at most IGS stations and regions. The RAIM availability of triple-satellite faults is lower than that of dual-satellite faults, and the RAIM availability of dual-satellite faults is lower than that of single-satellite fault. For the three different faults, the RAIM availability cannot fully satisfy the four stages in aviation all over the world. Among them, the RAIM availability is highest in the en-route (oceanic/continental low density) stage for all three types of faults, which is 93.146%, 74.848%, and 67.576%, respectively, while the non-precision approach stage has the lowest RAIM availability, which is 78.193%, 13.636%, 3.333% for the three types of faults, respectively.

C<sup>3</sup>ONTEXT: a Common Consensus on Convective OrgaNizaTion during the EUREC<sup>4</sup>A eXperimenT

Abstract. The C3ONTEXT (a Common Consensus on Convective OrgaNizaTion during the EUREC4A eXperimenT) dataset is presented as an overview of the mesoscale cloud patterns identified during the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) field campaign in early 2020. Based on infrared and visible satellite images, 50 researchers of the EUREC4A science team manually identified the four prevailing mesoscale patterns of shallow convection observed by Stevens et al. (2020). The common consensus on the observed mesoscale cloud patterns emerging from these manual classifications is presented. It builds the basis for future studies and reduces the subjective nature of these visually defined cloud patterns. This consensus makes it possible to contextualize the measurements of the EUREC4A field campaign and interpret them in the mesoscale setting. Commonly used approaches to capture the mesoscale patterns are computed for comparison and show good agreement with the manual classifications. All four patterns as classified by Stevens et al. (2020) were present in January–February 2020, although not all were dominant during the observing period of EUREC4A. Supplemental classifications of storm-resolving simulations suggest that the latter have a limited ability to replicate the observed cloud patterns and require further research. The full dataset including post-processed datasets for easier usage are openly available at the Zenodo archive at https://doi.org/10.5281/zenodo.5979718 (Schulz, 2022a).

Embedding-Based Similarity Computation for Massive Vehicle Trajectory Data

Trajectory similarity computation is a fundamental problem for many intelligent applications such as trip trajectory mining to find the most popular routes and road anomaly detection. Existing similarity computation methods for vehicle trajectory, such as dynamic time warping (DTW) and Fréchet distance, are dynamic programming problems with a quadratic time complexity in all cases and need to handle local time shifts when computing the distance between trajectories, thus limiting the scalability of these methods. In addition, GPS-based vehicle trajectories usually contain errors, such as noise and outliers, due to poor satellite visibility in urban regions and nonuniform sampling rates. To this end, we propose an embedding-based method for trajectory similarity computation with linear time complexity, which encodes a trajectory as a vector via deep representation learning and learns the similarity between trajectories with an attention-based learning to rank model. We use an interpolation-based approach to reduce noise and outliers by considering vehicle trajectory is physically constrained to the road network. Experiments on a massive vehicle trajectory data set show that the proposed approach outperforms state-of-the-art baselines consistently and significantly.

Low-Orbit Large-Scale Communication Satellite Constellation Configuration Performance Assessment

A constellation configuration performance evaluation method is proposed for the performance evaluation of the low-orbit large-scale communication satellite constellations. The practicality and feasibility analysis of the constellation configuration is mainly studied from the constellation coverage performance. Based on the consideration of the coverage performance of the LEO satellite constellation, four simulation models are established for the single coverage rate, observation elevation angle, number of visible satellites under different observation elevation angles, and coverage efficiency of the constellation. A population distribution density function is established according to the characteristics of population distribution to find the average minimum observation elevation angle and the average number of visible satellites under the population distribution. The evaluation method is applied to three typical low-orbit large-scale communication satellite constellations, Telesat, OneWeb, and Starlink, to derive the coverage performance index values of each constellation and to compare and analyze the characteristics of the three constellations. The results show that the evaluation method can evaluate the configuration performance of different types of LEO large-scale constellations and provide a basis and reference for the optimal design and evaluation of future LEO large-scale constellation configurations.

Signal quality and positioning performance of GPS/BDS-3/GLONASS/Galileo in polar regions

Growing activities in the Arctic and Antarctica call for more navigation and positioning services in these regions. The current GNSS (Global Navigation Satellite System) constellations, which have the capability of fully global positioning services, consist of GPS, BDS-3, GLONASS and Galileo. In this study, multi-GNSS observations of 2 stations from international GNSS monitoring and assessment system (iGMAS) and 10 stations from multi-GNSS experiment (MGEX) are selected to assess the signal quality and positioning performance of multi-GNSS in polar regions. After the fully operational global services of BDS-3 from July 31, 2020, this is the first time to assess the positioning performance of GPS/BDS-3/GLONASS/Galileo in polar regions. For BDS-3 and GPS, its number of visible satellites (NVS) is between 6 and 12 and position dilution of precision (PDOP) is approximately 2, which is slightly better than that of GLONASS and Galileo. As for signal to noise ratio (SNR) and multipath (MP) delay, each frequency of BDS/GPS/GLONASS/Galileo exhibits generally similar strength, and among them, the B2a signal of BDS-3 has stronger anti-multipath ability. In terms of positioning performance, the results from solution independent exchange (SINEX) are taken as the reference for more objective validation. For single-constellation, the precise point positioning (PPP) accuracy of BDS-3 is at the level of centimeters, which is equivalent to GPS, GLONASS, and Galileo. The multi-constellation PPP was analyzed with double/triple/quadruple-constellation combinations. Compared to single-constellation, the multi-constellation combination slightly improves positioning accuracy, and as the number of available satellites in multi-constellation combinations greatly increases, the convergence is accelerated.

A collaborative navigation method based on partnership optimisation for clustered aircraft

The collaborative exchange of information between members of a cluster can provide additional observations to improve the reliability of navigation. To address the problems of poor geometric configurations of satellites and low positioning accuracy of satellite navigation systems in complex environments, this paper proposes a collaboration-augmented navigation method with optimisation of the geometric dilution of precision for a clustered aircraft. A collaboration-augmented navigation model is established in which the differences between the characteristics of satellite pseudorange measurements and collaborative information measurements are taken into consideration to overcome heteroscedasticity. Then, an optimal collaboration-augmented navigation algorithm is proposed on the basis of geometric configuration analysis. In this method, all visible satellites are projected onto the current horizontal plane, and in accordance with the satellite distribution, a satellite reference ring is constructed to determine the optimal collaborative area for an aircraft. Then, a collaborative aircraft selection is made to select an optimal aircraft to assist the satellite navigation system for configuration optimisation. Finally, the algorithm performance is simulated and verified by simulation, and the influence of the equivalent ranging error on the effectiveness of geometric configuration optimisation is analysed, indicating the effectiveness of the proposed method.

A novel partial ambiguity resolution based on ambiguity dilution of precision- and convex-hull-based satellite selection for instantaneous multiple global navigation satellite systems positioning

AbstractAlthough multiple global navigation satellite systems (multi-GNSS) with more visible satellites have a high success rate, they make positioning time-consuming. Partial ambiguity resolution (PAR) can improve the efficiency of multi-GNSS; however, at present PAR cannot simultaneously achieve fast and high-precision positioning with a high success rate. Therefore, PAR based on ambiguity dilution of precision- and convex-hull-based satellite selection is proposed. The experimental results of the proposed PAR, its corresponding satellite selection algorithm, the classical PAR, and the low-cutoff-elevation-angle-based multi-GNSS show that the proposed PAR outperforms the classical PAR, i.e., it achieves fast and high-precision positioning with a success rate of 100⋅0%. Furthermore, in terms of R-ratio-test-based ambiguity validation, it improves the reliability of carrier-phase-based integrity monitoring of multi-GNSS and the corresponding satellite selection algorithms. In addition, its positioning accuracy is close to that of multi-GNSS and higher than that of the classical PAR, with maximum differences of 0⋅3 and 2⋅4 cm, respectively. The proposed single (dual) frequency-based PAR improves single/dual-frequency multi-GNSS efficiency by more than 54⋅9%/80⋅4% (42⋅0%/75⋅8%) when 14⋅4 (13⋅2) out of 24⋅4 satellites are selected.

A Modelling Study by Factorial Design on GNSS Positioning

Although researchers have widely studied the analysis and modelling of error sources on Global Navigation Satellite Systems positioning, some of these errors have not been eliminated significantly and only some of the Global Navigation Satellite System’s data are modelled. The present work was undertaken to determine the effect of different variables, namely: season, the number of visible satellites, and dilution of precision on the efficiency of horizontal and vertical CORS (Continuously Operating Reference Stations) positioning. For this aim, the CORS data was collected at 14 different test points during 600 epochs with 1-second intervals. Factorial designs supply an efficient solution to understand the impact of several factors on a response variable. A full factorial design with three factors at two levels was applied for these purposes. According to the results of the full factorial design, all factors significantly affected the response variable. Also, the interaction effects of factors were analysed on the CORS horizontal and vertical positioning. The regression equations were obtained for all situations.

The Brettenham storm of 25 July 2021

The <scp>Brettenham</scp> storm of 25 <scp>July</scp> 2021

Investigation of Using Sky Openness Ratio as Predictor for Navigation Performance in Urban-like Environment to Support PBN in UTM.

One of the causes of positioning inaccuracies in the Unmanned Aircraft System (UAS) is navigation error. In urban environment operations, multipaths could be the dominant contributor to navigation errors. This paper presents a study on how the operation environment affects the lateral (horizontal) navigation performance when a self-built UAS is going near different types of urban obstructions in real flight tests. Selected test sites are representative of urban environments, including open carparks, flight paths obstructed by buildings along one or both sides, changing sky access when flying towards corners formed by two buildings or dead ends, and buildings with reflective glass-clad surfaces. The data was analysed to obtain the horizontal position error between Global Positioning System (GPS) position and ground truth derived from Real Time Kinematics (RTK), with considerations for (1) horizontal position uncertainty estimate (EPH) reported by the GPS receiver, (2) no. of visible satellites, and (3) percentage of sky visible (or sky openness ratio, SOR) at various altitudes along the flight paths inside the aforementioned urban environments. The investigation showed that there is no direct correlation between the measured horizontal position error and the reported EPH; thus, the EPH could not be used for the purpose of monitoring navigation performance. The investigation further concluded that there is no universal correlation between the sky openness ratio (SOR) seen by the UAS and the resulting horizontal position error, and a more complex model would need to be considered to translate 3D urban models to expected horizontal navigation uncertainty for the UAS Traffic Management (UTM) airspace.

Combined positioning algorithm based on BeiDou navigation satellite system and raw 5G observations

In environments such as urban canyons and indoor environments, an insufficient number of visible satellites can degrade the positioning performance of the BeiDou Navigation Satellite System (BDS). Thus, a positioning algorithm that combines the BDS and 5G technology was developed based on raw observation data. Three different satellite occlusion environments were simulated to compare the effects of factors such as the number of base stations and geometric configurations on the positioning performance. The experimental results revealed that the introduction of 5G base stations effectively increased the positioning accuracy and reliability under different occlusion conditions. Additionally, increasing the number of base stations and optimizing the geometric configuration further improved the accuracy and reliability of fusion localization. Moreover, the positive effects of the introduction of 5G base stations were stronger under more severe satellite occlusion conditions. In the future, this fusion positioning algorithm is expected to facilitate the realization of seamless indoor and outdoor positioning.

TrackInk: An IoT-Enabled Real-Time Object Tracking System in Space

Nowadays, there is tremendous growth in the Internet of Things (IoT) applications in our everyday lives. The proliferation of smart devices, sensors technology, and the Internet makes it possible to communicate between the digital and physical world seamlessly for distributed data collection, communication, and processing of several applications dynamically. However, it is a challenging task to monitor and track objects in real-time due to the distinct characteristics of the IoT system, e.g., scalability, mobility, and resource-limited nature of the devices. In this paper, we address the significant issue of IoT object tracking in real time. We propose a system called ‘TrackInk’ to demonstrate our idea. TrackInk will be capable of pointing toward and taking pictures of visible satellites in the night sky, including but not limited to the International Space Station (ISS) or the moon. Data will be collected from sensors to determine the system’s geographical location along with its 3D orientation, allowing for the system to be moved. Additionally, TrackInk will communicate with and send data to ThingSpeak for further cloud-based systems and data analysis. Our proposed system is lightweight, highly scalable, and performs efficiently in a resource-limited environment. We discuss a detailed system’s architecture and show the performance results using a real-world hardware-based experimental setup.

Use of Hyper-Spectral Visible and Near-Infrared Satellite Data for Timely Estimates of the Earth’s Surface Reflectance in Cloudy and Aerosol Loaded Conditions: Part 1–Application to RGB Image Restoration Over Land With GOME-2

Space-based quantitative passive optical remote sensing of the Earth’s surface typically involves the detection and elimination of cloud-contaminated pixels as an initial processing step. We explore a fundamentally different approach; we use machine learning with cloud contaminated satellite hyper-spectral data to estimate underlying terrestrial surface reflectances at red, green, and blue (RGB) wavelengths. An artificial neural network (NN) reproduces land RGB reflectances with high fidelity, even in scenes with moderate to high cloud optical thicknesses. This implies that spectral features of the Earth’s surface can be detected and distinguished in the presence of clouds, even when they are partially and visibly obscured by clouds; the NN is able to separate the spectral fingerprint of the Earth’s surface from that of the clouds, aerosols, gaseous absorption, and Rayleigh scattering, provided that there are adequately different spectral features and that the clouds are not completely opaque. Once trained, the NN enables rapid estimates of RGB reflectances with little computational cost. Aside from the training data, there is no requirement of prior information regarding the land surface spectral reflectance, nor is there need for radiative transfer calculations. We test different wavelength windows and instrument configurations for reconstruction of surface reflectances. This work provides an initial example of a general approach that has many potential applications in land and ocean remote sensing as well as other practical uses such as in search and rescue, precision agriculture, and change detection.

GNSS-5G Hybrid Positioning Based on Multi-Rate Measurements Fusion and Proactive Measurement Uncertainty Prediction

The fifth-generation (5G) network can be used jointly with a global navigation satellite system (GNSS) to help relieve the problem of having few visible satellites in urban environments and achieve highly accurate localization. The 5G base stations (BSs) can provide range and angle measurements at a much higher rate than GNSS. However, this benefit has not been fully employed by existing GNSS-5G hybrid methods. In addition, the near–far effect of 5G BS causes a large dynamic range of the measurement noise uncertainty. This challenge has not been well dealt with by the existing adaptive noise estimation methods, resulting in the decline of positioning accuracy and system convergence. This work proposes a multiple-rate adaptive Kalman filter (MRAKF) for GNSS-5G hybrid positioning with a hybrid sequential fusing scheme. It supports the fusion of GNSS and 5G measurements at different data rates to better employ the high-rate 5G measurements. A distance-dependent model is derived to quantitatively characterize the effect of BS-user equipment (UE) distance over the measurement noise covariance, leading to a proactive measurement uncertainty prediction algorithm to adaptively adjust the observation noise covariance matrix. Furthermore, a switchover strategy is proposed to switch its positioning mode to avoid the impact of variation of available line of sight (LOS) BSs. Both simulations and a real driving test were carried out. Results show that the proposed MRAKF method significantly improves the positioning accuracy compared with the other four adaptive noise estimation methods and the standard EKF. It requires the lowest computation complexity among all adaptive methods in this work.

A Method for dSTEC Interpolation: Ionosphere Kernel Estimation Algorithm

Ionospheric structure is important for estimating ionospheric delay for user stations in Global Navigation Satellite System (GNSS). However, most existing parameter estimation methods suffer from challenges due to data inaccuracy and unavailability of limited and sparse scattered data at ground reference stations. The high variability of active low latitude or disturbed ionosphere leads to GNSS signal scintillation. It is critical to capture ionospheric random structure and estimate ionospheric parameter using data of disperse receivers to improve accuracy. This paper proposes a unifying method named Ionosphere Kernel Estimation Algorithm (IKEA) to retrieve the information of ionospheric spatial structure. The proposed model utilities the semi-parametric representation theorem to incorporate prior information and constraints. The multiple kernel technique is adopted firstly to include physical correlations. Additionally, a learning approach is deployed to determine model parameters. The IKEA model has been verified based on simulated and experimental data at active low latitudes from a network of ground GNSS reference stations from all visible GPS and GALILEO satellites. The IKEA model reduces approximately 19.5% and 24.2% of differential Slant Total Electron Content (dSTEC) in the root mean square error with respect to Inverse Distance Weighting (IDW) and the Kriging model during high ionospheric activities. The IKEA architecture has been demonstrated effective to make robust ionospheric estimation, which may be further extended for various GNSS applications and beyond.

Low- and Moderate-Level RFI Detection Using Point-Source Ripple for Synthetic Aperture Interferometric Radiometer

Finding out and turning off the radio-frequency interference (RFI) sources as many as possible is an important work for synthetic aperture interferometric radiometer (SAIR). While the impact of strong RFIs on reconstructed brightness temperature (BT) image is clearly observable, the presence of low- or moderate-level RFIs is often hard to be aware of, leaving the RFI detection and localization a difficult problem. In this article, an improved RFI detection method is proposed by making use of the point-source ripple to give prominence to the RFIs presented in the SAIR imagery. The method tests based on Soil Moisture and Ocean Salinity satellite (SMOS) visibility samples and the performance evaluations by numerical simulations validate the correctness and effectiveness of the developed method.

Processing of the Results of Satellite Inland Positioning Transport Using Mobile Devices and Their Visualization

The issue of spatial data visualization is currently an important element in the positioning and navigation process. The constant trend in increasing the accuracy and availability of position modules affects the widespread use of the mobile devices in transport. The paper presents creation of a three-dimensional visualization model based on ground tracks recorded in NMEA (National Marine Electronics Association) and GPX (GPS Exchange Format) formats. Additionally, the study presents an analysis of the positioning accuracy including the sky obstructions presence and the instantaneous state of the satellite constellation. The significant deterioration in positioning accuracies was noted due to the presence of sky obstructions and low movement speed during data recording. The analysis of these parameters showed the dependence of the positioning accuracy with the number of visible satellites and the HDOP (Horizontal Dilution of Precision) parameter.

Detection of Surface Water Bodies and Cyanobacterial Blooms using Satellite based Remote Sensing

For many environmental applications, accurate surface water maps are crucial. It is possible to construct surface water maps by merging data from multiple sources. Due to sensor limitations, complex land cover, geography, and atmospheric conditions, accurate surface water estimation using satellite imagery is still a difficult undertaking. In this study, Sentinel 1 images from Google Earth Engine (GEE) was used to derive a high-resolution water mask. To overcome the limitation of visible satellite images which is the presence of clouds, on this research was focused on using Synthetic-Aperture Radar (SAR) sensors, like Sentinel-1, which penetrate clouds and permitting to get a view of the earth surface, even when clouds are presence. To validate the proposed method, it was applied to three study cases which are located in different climate zones in Sri Lanka. In the meanwhile, this research implemented a time series of Sentinel 2 satellite images from GEE to monitor the distribution of cyanobacterial blooms on those lakes. Recent years have seen an increase in the frequency and severity of cyanobacteria in recreational lakes and reservoirs, which has become a serious concern for the public’s health and a serious environmental danger. Monitoring cyanobacterial blooms serves as the foundation for early detection and treatment. Therefore, the results of this study could be used as a benchmark for future environmental monitoring and management of these Sri Lankan lakes and reservoirs.