Current Satellite Systems Research Articles

A comprehensive systematic review of privacy and security issues in Satellite Networks

The recent advancements in satellite networks have garnered significant interest due to their extensive geographic coverage and flexible deployment capabilities, offering a promising solution for global communications and transforming traditional communication methods. Despite these advancements, current satellite systems face challenges such as high propagation delays and inadequate coverage of high-latitude regions, particularly in Geostationary (GEO) systems. Low Earth Orbit (LEO) systems, which can address these issues, are primarily used for voice services, as seen in the Iridium system, but have encountered financial difficulties. This study aims to address the security issues in satellite networks, a critical concern as these networks increasingly rely on IP protocols and hybrid configurations of terrestrial nodes and satellite links. Previous works have identified various potential security attacks on satellite networks and proposed different solutions, but these solutions often lack comprehensive effectiveness and robustness. Our methodology involves analyzing the security vulnerabilities in satellite networks similar to the Iridium system, which includes inter-satellite links (ISL) and routers on each satellite. We review and evaluate existing security measures and propose enhancements to improve their effectiveness. Our results indicate significant vulnerabilities in current systems, but also show that with targeted improvements, security can be substantially enhanced. The implications of this study are profound, suggesting that more secure satellite networks can better support critical global communications and services, including broadband Internet and data services, thereby enhancing their reliability and user trust

Sentinel-1 detection of ice slabs on the Greenland Ice Sheet

Abstract. Ice slabs are multi-meter-thick layers of refrozen ice that limit meltwater storage in firn, leading to enhanced surface runoff and ice sheet mass loss. To date, ice slabs have primarily been mapped using airborne ice-penetrating radar, which has limited spatial and temporal coverage. This makes it difficult to fully assess the current extent and continuity of ice slabs or to validate predictive models of ice slab evolution that are key to understanding their impact on Greenland's surface mass balance. Here, for the first time, we map the extent of ice slabs and superimposed ice facies across the entire Greenland Ice Sheet at 500 m resolution using dual-polarization Sentinel-1 (S-1) synthetic-aperture radar (SAR) data collected in winter 2016–2017. We do this by selecting empirical thresholds for the cross-polarized backscatter ratio and HV backscattered power that jointly optimize the agreement between airborne ice-penetrating radar data detections of ice slabs and the S-1 estimates of ice slab extent. Our results show that there is a strong correlation between C-band backscatter and the ice content of the upper ∼ 7 m of the firn column that enables ice slab mapping with S-1. Our new mapping shows that ice slabs are nearly continuous around the entire margin of the ice sheet. This includes regions in southwest Greenland where ice slabs have not been previously identified by ice-penetrating radar but where the S-1-inferred ice slab extent shows strong agreement with the extent of visible runoff mapped from optical imagery. The algorithm developed here lays the groundwork for the long-term monitoring of ice slab expansion with current and future C-band satellite systems and highlights the potential added value of future L-band missions for near-surface studies in Greenland.

Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean

Abstract. Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and the nonlinear dependence of the forward model on the retrieved parameters near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on efficient neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the uncertainty evaluation technique in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, as well as guide MAP algorithm development for current and future satellite systems such as NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

Engineering Poster E4: Strength in numbers: how the different satellite systems used to monitor methane emissions from space have different, yet complementary, capabilities to help the oil and gas industry meet its decarbonisation goals

Poster E4 Satellites are a powerful tool in monitoring methane emissions around the world. In the last 5 years, many new systems have been both announced and deployed, each with different capabilities, and designed for a specific purpose. With an increase in options also comes confusion as to how these systems can and should be used. This paper will examine how these systems complement each other’s strengths and weaknesses to provide actionable insight to the oil and gas industry. The performance parameters of several current and future satellite systems will be presented and compared, supported with recent examples when available. The importance of factors like frequency of revisit, detection threshold, precision, and spatial resolution will be discussed and contrasted with the needs of the oil and gas industry in gaining a more complete understanding of its methane emissions, in providing key information to stakeholders, and in enabling action to mitigate emissions. Results from GHGSat’s second generation of high-resolution satellites displaying measurements of methane plumes at oil and gas facilities around the world will be presented to demonstrate some of the advantages of the technology. These two satellites, GHGSat-C1 and C2 (Iris and Hugo), were launched in September 2020 and January 2021, respectively. Another eight satellites are planned to be launched by mid-2023. Finally, the ability of these systems to work together and complement each other’s capabilities, and some of the analytics tools used to augment the data, will be presented. To access the poster click the link on the right. To read the full paper click here

Concurrent 22. Presentation for: Strength in numbers: how the different satellite systems used to monitor methane emissions from space have different, yet complementary, capabilities to help the oil and gas industry meet its decarbonisation goals

Presented on Thursday 19 May: Session 22 Satellites are a powerful tool in monitoring methane emissions around the world. In the last 5 years, many new systems have been both announced and deployed, each with different capabilities, and designed for a specific purpose. With an increase in options also comes confusion as to how these systems can and should be used. This paper will examine how these systems complement each other’s strengths and weaknesses to provide actionable insight to the oil and gas industry. The performance parameters of several current and future satellite systems will be presented and compared, supported with recent examples when available. The importance of factors like frequency of revisit, detection threshold, precision, and spatial resolution will be discussed and contrasted with the needs of the oil and gas industry in gaining a more complete understanding of its methane emissions, in providing key information to stakeholders, and in enabling action to mitigate emissions. Results from GHGSat’s second generation of high-resolution satellites displaying measurements of methane plumes at oil and gas facilities around the world will be presented to demonstrate some of the advantages of the technology. These two satellites, GHGSat-C1 and C2 (Iris and Hugo), were launched in September 2020 and January 2021, respectively. Another eight satellites are planned to be launched by mid-2023. Finally, the ability of these systems to work together and complement each other’s capabilities, and some of the analytics tools used to augment the data, will be presented. To access the presentation click the link on the right. To read the full paper click here

Strength in numbers: how the different satellite systems used to monitor methane emissions from space have different, yet complementary, capabilities to help the oil and gas industry meet its decarbonisation goals

Satellites are a powerful tool in monitoring methane emissions around the world. In the last 5 years, many new systems have been both announced and deployed, each with different capabilities, and designed for a specific purpose. With an increase in options also comes confusion as to how these systems can and should be used. This paper will examine how these systems complement each other’s strengths and weaknesses to provide actionable insight to the oil and gas industry. The performance parameters of several current and future satellite systems will be presented and compared, supported with recent examples when available. The importance of factors like frequency of revisit, detection threshold, precision, and spatial resolution will be discussed and contrasted with the needs of the oil and gas industry in gaining a more complete understanding of its methane emissions, in providing key information to stakeholders, and in enabling action to mitigate emissions. Results from GHGSat’s second generation of high-resolution satellites displaying measurements of methane plumes at oil and gas facilities around the world will be presented to demonstrate some of the advantages of the technology. These two satellites, GHGSat-C1 and C2 (Iris and Hugo), were launched in September 2020 and January 2021, respectively. Another eight satellites are planned to be launched by mid-2023. Finally, the ability of these systems to work together and complement each other’s capabilities, and some of the analytics tools used to augment the data, will be presented.

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

Reducing satellite failures and keeping satellites healthy in orbit are important issues. Current satellite systems have developed modules to detect anomalies on board. However, they only target a subset of anomaly types and heavily rely on expert knowledge. To address these limitations, this paper proposes a data-driven anomaly detection framework to detect point anomalies. We first propose the Deviation Divide Mean over Neighbors (DDMN) method to figure out the fake anomaly problem caused by data errors in the satellite telemetry data. Then, we use the Long Short-Term Memory (LSTM), a deep learning method, to model the multivariable time-series data, and a Gaussian model to detect anomalies. We applied our approach to the telemetry data collected from sensors on an in-orbit satellite for more than two years and demonstrate its superiority. Moreover, we explored what conditions could lead to false alarms. The approach proposed has been deployed to the ground station to monitor the health status of the in-orbit satellites.

Satellite Image Compression and Denoising With Neural Networks

Earth observation through satellite images is crucial to help economic activities as well as to monitor the impact of human activities on ecosystems. Current satellite systems are subjected to strong computational complexity constraints. Thus, image compression is performed onboard with specifically tailored algorithms while image denoising is performed on the ground. In this letter, we intend to address satellite image compression and denoising with neural networks. The first proposed approach uses a single neural architecture for joint onboard compression and denoising. The second proposed approach sequentially uses a first neural architecture for onboard compression and a second one for on ground denoising. For both approaches, the onboard architectures are lightened as much as possible, following the procedure proposed by Alves de Oliveira <i>et al.</i> (2021). The two approaches are shown to outperform the current satellite imaging system and their respective pros and cons are discussed.

AMSS in Europe

Nowadays, technology is developing rapidly, therefore satellite and aviation systems need to keep pace with modern trends and new technology. The paper is focused on satellite systems and aviation systems. Based on analysis of the issues it is possible to take measures against problems that may arise in the future. There is a need for satellite-based navigation system that can solve the problems of the existing systems and make the existing systems better and more efficient by providing great convenience to the airspace users for the safe, efficient, comfortable and economical realisation of flights in the future. The paper contains information about Air Communication and Navigation Systems and discusses the state of current aviation and satellite systems.

Interference-Aware Demand-Based User Scheduling in Precoded High Throughput Satellite Systems

In recent years, dynamic traffic demand requisites have driven the satellite communication service providers to implement reconfigurable demand-driven features to align the delivered throughput with the temporal and geographical variations of the traffic demand. Also, in current interference-limited High Throughput Satellite (HTS) systems, the resulting inter-beam co-channel interference can be mitigated by carefully performing precoding and user scheduling. Unfortunately, the conventional user scheduling algorithms fail to provide demand satisfaction for dynamic traffic demand requisites. Hence, in this paper, we focus on the user scheduling design for precoded satellite systems where both co-channel interference and user demands are taken into account. In particular, we first classify the sectors in each beam according to the interference they may cause to neighboring beams. Next, we formulate the scheduling problem such as the activation of neighboring beam sectors is avoided while proportionally dwelling on the sectors based on their traffic demands. The supporting numerical results for different demand distribution profiles validate the effectiveness of proposed interference-aware demand-based user scheduling over conventional scheduling techniques.

Integrated fusion framework based on semicoupled sparse tensor factorization for spatio-temporal–spectral fusion of remote sensing images

Remote sensing image fusion is considered a cost effective method for handling the tradeoff between the spatial, temporal and spectral resolutions of current satellite systems. However, most current fusion methods concentrate on fusing images in two domains among the spatial, temporal and spectral domains, and a few efforts have been made to comprehensively explore the relationships of spatio-temporal–spectral features. In this study, we propose a novel integrated spatio-temporal–spectral fusion framework based on semicoupled sparse tensor factorization to generate synthesized frequent high-spectral and high-spatial resolution images by blending multisource observations. Specifically, the proposed method regards the desired high spatio-temporal–spectral resolution images as a four-dimensional tensor and formulates the integrated fusion problem as the estimation of the core tensor and the dictionary along each mode. The high-spectral correlation across the spectral domain and the high self-similarity (redundancy) features in the spatial and temporal domains are jointly exploited using the low dimensional and sparse core tensors. In addition, assuming that the sparse coefficients in the core tensors across the observed and desired image spaces are not strictly the same, we formulate the estimation of the core tensor and the dictionaries as a semicoupled sparse tensor factorization of available heterogeneous spatial, spectral and temporal remote sensing observations. Finally, the proposed method can exploit the multicomplementary spatial, temporal and spectral information of any combination of remote sensing data based on this single unified model. Experiments on multiple data types, including spatio-spectral, spatio-temporal, and spatio-temporal–spectral data fusion, demonstrate the effectiveness and efficiency of the proposed method.

Development of stratospheric communication platforms (SCP) as backbone to terrestrial networks

&lt;p align="LEFT"&gt;&lt;span style="font-size: xx-small;"&gt;In this paper are introduced the new airship techniques and technologies as cost effective solutions of Stratospheric Communication Platforms (SCP) as future backbone of terrestrial networks for rural communications. The launch or putting in position the airship is not critical point such as launch of satellite and controlling support services in the creation of space-based communication technology and the most expensive phase of the total system cost. Therefore, with few cost effective remote controlled and solar powered airships can be covered some region or country including remote and rural areas with low density of population. The airship SCP network offers better solutions than cellular radio systems, with greater speed of transmission than even optical modes, roaming will be enhanced without severe shadowing or obstacle problems and disturbances inside of buildings and service will cost less. The SPS mission system is more autonomous and discrete, can be integrated with current satellite and cellular systems, and will be the best solution for rural, mobile transportation and military applications. The SCP airship can be seen well from all positions inside coverage area, because they are overlapping the total coverage and because of elevation angle. In any circumstances mountains, buildings and even trees cannot cause obstructions like to cellular network. For these reasons, there is currently a revival of interest for SCP constellations and application types of various system concepts are being studied.&lt;/span&gt;&lt;/p&gt;&lt;strong&gt;&lt;strong&gt;&lt;/strong&gt;&lt;/strong&gt;&lt;p&gt; &lt;/p&gt;&lt;strong&gt;&lt;/strong&gt;

Energy Minimization of Resource Allocation in Cloud-Based Satellite Communication Networks

In this letter, we give a cloud-based satellite communication network (C-SCN) architecture to make full utilization of resources in current satellite systems. Since the complex computation and transmission bring huge energy cost, energy minimization is one of the most important problems in the C-SCN. Considering that the energy minimization problem is involved with the delay constraint, we decompose the original problem into two coupling subproblems, virtual machine assignment and power allocation, and then propose a joint optimization algorithm to solve the subproblems. Our simulation results show that the proposed algorithm is superior to the existing algorithms on energy efficiency in the C-SCN.

Use of Cyclone Global Navigation Satellite System (CyGNSS) Observations for Estimation of Soil Moisture

AbstractUsing the first full annual cycle of Cyclone Global Navigation Satellite System (CyGNSS) observations, we investigated the limitations and capabilities of CyGNSS observations for soil moisture (SM) estimates (0–5 cm). A relative signal‐to‐noise ratio (rSNR) value from a CyGNSS‐derived delay‐Doppler map is introduced to improve the temporal resolution of SM derived from Soil Moisture Active Passive (SMAP) data. We then evaluated the CyGNSS‐derived rSNR using ground‐based SM measurements and the triple collocation method with SMAP and modeled SM products. We found that CyGNSS can provide useful SM estimates over moderately vegetated regions (correlation coefficient of the individual data: 0.77) but shows degraded performance over arid and densely vegetated regions (correlation coefficient of the individual data: 0.68 and 0.67). However, when rSNR data is combined with SM data from SMAP, daily SM estimates can be achieved. These results show that synergistic use of CyGNSS observations can improve on SM estimates from current satellite systems.

Spectrum Sharing Backhaul Satellite-Terrestrial Systems via Analog Beamforming

Current satellite and terrestrial backhaul systems are deployed in disjoint frequency bands. This fact precludes an efficient use of the spectrum and limits the evolution of wireless backhauling networks. In this paper, we propose an interference mitigation technique in order to allow the spectrum coexistence between satellite and terrestrial backhaul links. This interference reliever is implemented at the terrestrial backhaul nodes, which are assumed to be equipped with multiple antennas. Due to the large bandwidth and huge number of antennas required in these systems, we consider pure analog beamforming. Precisely, we assume a phased array beamforming configuration so that the terrestrial backhaul node can only reduce the interference by changing the phases of each beamforming weight. Two cases are considered: the 18 and 28 GHz band where transmit and receive beamforming optimization problems shall be tackled, respectively. In both cases, the optimization problem results in a nonconvex problem that we propose to solve via two alternative convex approximation methods. These two approaches are evaluated and they present less than 1 dB array gain loss with respect to the upper bound solution. Finally, the spectral efficiency gains of the proposed spectrum sharing scenarios are validated in numerical simulations.

Multi-GNSS signal-in-space range error assessment – Methodology and results

The positioning accuracy of global and regional navigation satellite systems (GNSS/RNSS) depends on a variety of influence factors. For constellation-specific performance analyses it has become common practice to separate a geometry-related quality factor (the dilution of precision, DOP) from the measurement and modeling errors of the individual ranging measurements (known as user equivalent range error, UERE). The latter is further divided into user equipment errors and contributions related to the space and control segment. The present study reviews the fundamental concepts and underlying assumptions of signal-in-space range error (SISRE) analyses and presents a harmonized framework for multi-GNSS performance monitoring based on the comparison of broadcast and precise ephemerides. The implications of inconsistent geometric reference points, non-common time systems, and signal-specific range biases are analyzed, and strategies for coping with these issues in the definition and computation of SIS range errors are developed. The presented concepts are, furthermore, applied to current navigation satellite systems, and representative results are presented along with a discussion of constellation-specific problems in their determination. Based on data for the January to December 2017 time frame, representative global average root-mean-square (RMS) SISRE values of 0.2 m, 0.6 m, 1 m, and 2 m are obtained for Galileo, GPS, BeiDou-2, and GLONASS, respectively. Roughly two times larger values apply for the corresponding 95th-percentile values. Overall, the study contributes to a better understanding and harmonization of multi-GNSS SISRE analyses and their use as key performance indicators for the various constellations.

Cost‐effective and novel regional navigation system using HEO/GEO and GEO/IGS Constellations

Current navigation satellite systems (Global Positioning System (GPS), GLObal'naya NAvigatsionnaya Sputnikovaya Sistema (GLONASS) and BeiDou-2) have enabled quality of life improvements on a global scale. The applications include in the fields of transportation, military, aviation and security etc. Annual business market value of Global Navigation Satellite System user equipment and related components has been estimated at billions of US dollars. To reap the benefits maximally, various countries are developing their Regional Navigation Satellite Systems (RNSSs). Independent RNSS guarantees uninterrupted and reliable services with an advantage of the customised coverage. This study presents the design of two constellation models, including a Highly Elliptical Orbit/Geostationary Orbit (HEO/GEO) and a Geostationary Orbit/Inclined Geosynchronous (GEO/IGS) constellation for Pakistan's regional navigation system. The performance analysis of the navigation ability of designed constellations is carried out by analysing various parameters such as number of satellites, Dilution of Precision and navigation accuracy in a constrained environment and comparing these parameters with the GPS using dynamic simulations. Navigation link budget of both constellations is theoretically calculated, verified and compared with that of the GPS. Parametric analysis proposes that HEO/GEO and GEO/IGS orbit-based constellations can be adopted as replacement of the GPS for independent navigation within Pakistan.

Digital Signal Processing of 400 Gbps CO-QPSK-WDM System Over Optical Wireless Channel for Carrier Phase Estimation

High bandwidth, low cost and small size has made Inter satellite optical wireless communication a promising and advance technology with an alternative to current microwave satellite systems. In this paper, advantages of coherent-detection quadrature phase shift keying (CO-QPSK) with DSP are investigated for the mitigation of non-linear effects. The technique of carrier phase estimation has been used to prolong the transmission range to several thousand kilometers. Further, 400 Gbps WDM-QPSK system has been analyzed for different distances with 30 dB transmitting power and 11 dB LO power. At last, system has been investigated at narrow channel spacing of 50 GHz and a brief comparison has been made with system having channel spacing of 100 GHz.

Improving BeiDou real-time precise point positioning with numerical weather models

Precise positioning with the current Chinese BeiDou Navigation Satellite System is proven to be of comparable accuracy to the Global Positioning System, which is at centimeter level for the horizontal components and sub-decimeter level for the vertical component. But the BeiDou precise point positioning (PPP) shows its limitation in requiring a relatively long convergence time. In this study, we develop a numerical weather model (NWM) augmented PPP processing algorithm to improve BeiDou precise positioning. Tropospheric delay parameters, i.e., zenith delays, mapping functions, and horizontal delay gradients, derived from short-range forecasts from the Global Forecast System of the National Centers for Environmental Prediction (NCEP) are applied into BeiDou real-time PPP. Observational data from stations that are capable of tracking the BeiDou constellation from the International GNSS Service (IGS) Multi-GNSS Experiments network are processed, with the introduced NWM-augmented PPP and the standard PPP processing. The accuracy of tropospheric delays derived from NCEP is assessed against with the IGS final tropospheric delay products. The positioning results show that an improvement in convergence time up to 60.0 and 66.7% for the east and vertical components, respectively, can be achieved with the NWM-augmented PPP solution compared to the standard PPP solutions, while only slight improvement in the solution convergence can be found for the north component. A positioning accuracy of 5.7 and 5.9 cm for the east component is achieved with the standard PPP that estimates gradients and the one that estimates no gradients, respectively, in comparison to 3.5 cm of the NWM-augmented PPP, showing an improvement of 38.6 and 40.1%. Compared to the accuracy of 3.7 and 4.1 cm for the north component derived from the two standard PPP solutions, the one of the NWM-augmented PPP solution is improved to 2.0 cm, by about 45.9 and 51.2%. The positioning accuracy for the up component improves from 11.4 and 13.2 cm with the two standard PPP solutions to 8.0 cm with the NWM-augmented PPP solution, an improvement of 29.8 and 39.4%, respectively.

Analysis of an Observing System Experiment for the Joint Polar Satellite System

Abstract The Joint Polar Satellite System (JPSS) is a key contributor to the next-generation operational polar-orbiting satellite observing system. In the JPSS era, the complete polar-orbiting observing system will be composed of two satellites—in the midmorning (mid-AM) and afternoon (PM) orbits—each with thermodynamic sounding capabilities from both microwave and hyperspectral infrared instruments. JPSS will occupy the PM orbit, while the Meteorological Operational (MetOp) system, sponsored by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), will occupy the mid-AM orbit. While the current polar-orbiting satellite system has been thoroughly evaluated, information about its resilience and efficacy in the JPSS era is needed. A 7-month (August 2012–February 2013) observing system experiment (OSE) was run with the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Observations were selected from operational satellite data platforms to be representative of the polar-orbiting data in the JPSS era. Overall, removing data from the PM orbit produced inferior scores, with the impact greater in the Southern Hemisphere (SH) than in either the Northern Hemisphere (NH) or the tropics. For the entire 7 months, the time-mean 500-hPa geopotential height anomaly correlation (Z500AC) decreased by 0.005 and 0.013 in the NH and SH, respectively—both of which are statistically significant at the 95% level. Additionally, a detailed statistical analysis of the distribution of Z500AC skill scores is presented and compared with historical accuracy data. It was determined that eliminating PM orbit data resulted in a higher probability of producing low scores and a lower probability of producing high scores, counter to the trend in GFS forecast skill over the last 20 years.