Satellite Resources Research Articles

Exploration of the application of optimisation algorithm using stochastic gradient descent method in satellite resource allocation

Exploration of the application of optimisation algorithm using stochastic gradient descent method in satellite resource allocation

A Two-Echelon Capacitated Vehicle Routing Problem With Sharing Satellite Resources

A Two-Echelon Capacitated Vehicle Routing Problem With Sharing Satellite Resources

Thermal design and flight validation of the chang’e-4 relay satellite

The Chang’e-4 relay satellite operates in the HALO orbit at the Earth-Moon Lagrange L2 point, which can provide relay communication to the lander and the rover operating on the lunar far side to maintain their contacts with the earth and is a key link of the Chang’e-4 exploration mission. Unlike most LEO (Low Earth Orbit) satellites, the outer space environment of the Chang’e-4 relay satellite is much harsher. The Chang’e-4 relay satellite must suffer from a torrid environment that faces direct sunlight or direct exposure to 4K deep cold space in a shadow zone which may last longer than 6 hours. The Chang’e-4 relay satellite must ensure the operation and temperature control of instruments and equipment during the shadow zone with a limited battery level. This proposes high demands on the TCD (Thermal Control Design). The entire satellite resources were reasonably used for integrated TCD with limited battery level and limited weight. According to the working mode of the Chang’e-4 relay satellite and its torrid environment, its TCD was completed successfully, and on-orbit test data conformed to the expected results and theoretical calculation. TCD of the Chang’e-4 relay satellite has worked normally since the day that the Chang’e-4 relay satellite was launched into space.

Software-Defined Satellite Observation: A Fast Method Based on Virtual Resource Pools

In recent years, the proliferation of remote sensing satellites has dramatically increased the demands of Earth observation and observing efficiency. Designing a promising satellite resource scheduling method is a pivotal way to meet the requirements of this scenario. However, with hundreds or more satellites involved, the existing optimization methods struggle to address the NP-hard resource scheduling problem effectively. In this paper, an approach named software-defined satellite observation (SDSO) is proposed. First, adopting the new design ideology, we define a unified specification based on a discrete spatial grid to describe the observation capability of all satellites. The observation resources are virtualized using the virtual resource pool technique and then stored in the database in advance, implementing on-demand acquisition for observation resources. Next, we designed a model of the remote sensing satellite resource scheduling problem based on a virtual resource pool and designed a solution method for searching information within the virtual resource pool. Finally, the experimental results show that the computational efficiency of the proposed SDSO methodology has a substantial advantage over the traditional methods. Meanwhile, with the growing number of satellites involved in scheduling, there is only a slight degradation in the execution performance of our method, while the time complexity of optimization-based approaches increases exponentially.

On-Board Geometric Rectification for Micro-Satellite Based on Lightweight Feature Database

On-board processing is increasingly prevalent due to its efficient utilization of satellite resources. Among these resources, geometric rectification can significantly enhance positioning accuracy for subsequent tasks, such as object detection. This approach mitigates the heavy burden on downlink bandwidth and minimizes time delays by transmitting targeted patches rather than raw data. However, existing rectification methods are often unsuitable due to the limitations and conditions imposed on satellites. Factors like hardware quality, heat dissipation, storage space, and geographic positioning are frequently constrained and prone to inaccuracies. This paper proposes a novel on-board rectification method. The method introduces a two-step matching framework to address substantial positioning errors and incorporates a feature-compression strategy to reduce the storage space of reference patches. Quantitative and practical experiments demonstrate the method’s efficacy in terms of storage space, time efficiency, and geometric rectification accuracy.

Wide-swath and high-resolution whisk-broom imaging and on-orbit performance of SDGSAT-1 thermal infrared spectrometer

Wide-swath and high-resolution Thermal infrared (TIR) data are limited by the mutual constraint between swath and resolution. In this paper, we propose a whisk-broom imaging method with the long-linear-array (LLA) 4-stage TDI detector (a total of 2048 pixels along splicing direction) and high-precision one-dimensional scanning mirror to achieve 30 m resolution and 300 km swath. And it is being implemented in the first SDGs satellite (SATSAT-1) thermal infrared spectrometer (TIS). Specially, the integration time of whisk-broom imaging system is limited by the speed of scanning mirror, and the shorter integration time is a huge challenge to meet the requirement of high sensitivity and dynamic range. The HgCdTe detector has been improved in its annealing process to reduce dark current and noise, and the opto-mechanical system has been regulated at 195 K to reduce instrument self-emission. To address the limitations of small satellite resources, TIS is equipped with a highly integrated and lightweight design, weighing 247.5 kg and consuming 248 watts respectively. As a result of the complexity of imaging mechanisms and the low frequency of deep space observations, positioning error and radiometric calibration resources have increased, and geometric and radiometric calibration methods have been improved. On-orbit test results show that TIS is capable of detecting temperature differences with a NEdT (noise equivalent differential temperature) less than 0.08 K@300 K, an absolute radiometric accuracy of less than 1 K @ 300 K, and MTF of greater than 0.125. Presently, a collection of more than 201,856 scenes covering land and marine and some precious TIR datasets of planes and naval vessels have been obtained and provided for United Nations for the realization of SDGs goals.

A Multiple Agile Satellite Staring Observation Mission Planning Method for Dense Regions

To fully harness the burgeoning array of in-orbit satellite resources and augment the efficacy of dynamic surveillance of densely clustered terrestrial targets, this paper delineates the following methodologies. Initially, we leverage the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to aggregate the concentrated terrestrial targets, taking into account the field-of-view peculiarities of agile staring satellites. Subsequently, we architect a model for a synergistic multiple angle earth observation satellites (AEOSs) mission planning with the optimization objectives of observational revenue, minimal energy expenditure, and load balancing, factoring in constraints such as target visibility time window, AEOSs maneuverability, and satellite storage. To tackle this predicament, we propose an improved heuristic ant colony optimization (ACO) algorithm, utilizing the task interval, task priority, and the length of time a task can start observation as heuristic information. Furthermore, we incorporate the notion of the max–min ant system to regulate the magnitude of pheromone concentration, and we amalgamate global and local pheromone update strategies to expedite the convergence rate of the algorithm. We also introduce the Lévy flight improved pheromone evaporation coefficient to bolster the algorithm’s capacity to evade local optima. Ultimately, through a series of simulation experiments, we substantiate the significant performance improvements achieved by the improved heuristic ant colony algorithm compared to the standard ant colony algorithm. We furnish proof of its efficacy in resolving the planning of multiple AEOS staring observation missions.

Differential evolutionary algorithm based on principal component analysis for the satellite bandwidth resource scheduling problem

This paper focuses on the research of the communication satellite bandwidth resource scheduling problem. Satellite resource scheduling refers to adjusting the order of task execution within a certain scheduling time to complete as many satellite tasks as possible while satisfying bandwidth and time constraints. Traditional algorithms cannot meet the time requirements in the process of satellite communication resource scheduling. Therefore, this paper proposes a differential evolution algorithm based on principal component analysis (PCA) and combines it with the Lowest Horizontal Line Algorithm (LHLA) to solve this problem. The method was validated on two datasets at 20 dimensions and 40 dimensions, and compared with other traditional DE algorithms. The experimental results showed that the method obtained better solutions.

Intelligent Hierarchical Admission Control for Low-Earth Orbit Satellites Based on Deep Reinforcement Learning.

Low-Earth orbit (LEO) satellites have limited on-board resources, user terminals are unevenly distributed in the constantly changing coverage area, and the service requirements vary significantly. It is urgent to optimize resource allocation under the constraint of limited satellite spectrum resources and ensure the fairness of service admission control. Therefore, we propose an intelligent hierarchical admission control (IHAC) strategy based on deep reinforcement learning (DRL). This strategy combines the deep deterministic policy gradient (DDPG) and the deep Q network (DQN) intelligent algorithm to construct upper and lower hierarchical resource allocation and admission control frameworks. The upper controller considers the state features of each ground zone and satellite resources from a global perspective, and determines the beam resource allocation ratio of each ground zone. The lower controller formulates the admission control policy based on the decision of the upper controller and the detailed information of the users' services. At the same time, a designed reward and punishment mechanism is used to optimize the decisions of the upper and lower controllers. The fairness of users' services admissions in each ground zone is achieved as far as possible while ensuring the reasonable allocation of beam resources among zones. Finally, online decision-making and offline learning were combined, so that the controller could make full use of a large number of historical data to learn and generate intelligent strategies with stronger adaptive ability while interacting with the network environment in real time. A large number of simulation results show that IHAC has better performance in terms of a successful service admission rate, service drop rate, and fair resource allocation. Among them, the number of accepted services increased by 20.36% on average, the packet loss rate decreased by 17.56% on average, and the resource fairness increased by 17.16% on average.

Comprehensive Assessment of Sustainable Development Goal 11 at the Sub-City Scale: A Case Study of Guilin City

Quantifying the progress and interactions of the 11 indicators of Sustainable Development Goal 11 plays a crucial role in improving urban living and promoting urban prosperity. SDG 11, focused on sustainable cities and communities, employs forward-thinking strategies to address challenges arising from urban prosperity and development, such as land scarcity and resource shortages. This paper positions the indicators of SDG 11, analyzing the patterns, trends, dynamics, and issues of urbanization development in Guilin using a combination of geospatial satellite resource data and categorical statistical data. The study introduces a framework and positioning method for assessing sustainable development at the city–county scale, exploring the current state, spatial aggregation, synergies, and trade-offs in the development of Guilin City. The study introduces a framework and positioning method for assessing sustainable development at the city–county scale. Utilizing a localized evaluation system, it explores the developmental status of Guilin City. The application of Moran’s Index observes spatial aggregation among entities. By investigating Spearman’s rank correlation coefficient, it delves into the interplay of synergies and trade-offs within the studied region. Ultimately, it reveals significant disparities in the developmental landscape of the evaluated area, with a comprehensive spatial distribution indicating higher levels of development in the central and western regions and lower levels in the southeastern part. Strengthened cross-leverage and coordination are imperative to address the interconnections and harmonization of the developmental trends of the six synergistic indicators and nine trade-off indicators during the developmental process. The sustainable development of Guilin lays the groundwork for urban planning, construction, conservation, and management, positioning it as a potential model for successful sustainable development practices.

Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping

With satellite quantity and quality development in recent years, remote sensing products in vast areas are becoming widely used in more and more fields. The acquisition of large regional images requires the scientific and efficient utilization of satellite resources through imaging satellite task planning technology. However, for imaging satellite task planning in a vast area, a large number of decision variables are introduced into the imaging satellite task planning model, making it difficult for existing optimization algorithms to obtain reliable solutions. This is because the search space of the solution increases the exponential growth with the increase in the number of decision variables, which causes the search performance of optimization algorithms to decrease significantly. This paper proposes a large-scale multi-objective optimization algorithm based on efficient competition learning and improved non-dominated sorting (ECL-INS-LMOA) to efficiently obtain satellite imaging schemes for large areas. ECL-INS-LMOA adopted the idea of two-stage evolution to meet the different needs in different evolutionary stages. In the early stage, the proposed efficient competitive learning particle update strategy (ECLUS) and the improved NSGA-II were run alternately. In the later stage, only the improved NSGA-II was run. The proposed ECLUS guarantees the rapid convergence of ECL-INS-LMOA in the early evolution by accelerating particle update, introducing flight time, and proposing a binary competitive swarm optimizer BCSO. The results of the simulation imaging experiments on five large areas with different scales of decision variables show that ECL-INS-LMOA can always obtain the imaging satellite mission planning scheme with the highest regional coverage and the lowest satellite resource consumption within the limited evaluation times. The experiments verify the excellent performance of ECL-INS-LMOA in solving vast area mapping planning problems.

Research on user requirements preprocessing of imaging reconnaissance satellites

Aiming at how to solve the problem of optimal matching between imaging reconnaissance user requirements and satellite resources, this paper conducts research on the preprocessing of imaging reconnaissance satellite requirements. First, considering the remote sensor type and resolution of imaging satellites, a requirement-resource matching matrix is constructed to realize the preliminary matching between user requirements and imaging reconnaissance satellite resources. Secondly, starting from the time constraints, by calculating the observation time window of requirement and resources, the time requirements of satellite resources and user requirements are matched. Finally, in order to solve the problem that the same observation task can be completed by multiple imaging satellites, a conflict resolution method based on resource consumption is proposed, which effectively improves the preprocessing ability of user requirements.

Climate change, large fires, and cultural landscapes in the mediterranean basin: An analysis in southern Spain

Understanding the factors that influence fire regimes in Mediterranean climates is essential to reduce their risk. This research uses Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite resources to evaluate recent changes in land surface temperature, precipitation, and vegetation and their effects in the occurrence of large fires in the Mediterranean Basin. The results of the analysis of 335 fire events occurred in southern Spain from 2001 to 2020 show an increase in hazardous meteorological factors linked to droughts and thermal anomalies. The study also examines the potential of preserving traditional landscapes to minimize such risk. In fact, the maintenance and recovering of traditional agro-pastoral activities is an effective option to reduce flammability and increase the resilience of cultural landscapes in hazardous climatic conditions.

NOMA-based collaborative beam hopping frequency allocation mechanism for future LEO satellite systems

Low Earth orbit (LEO) satellite systems provide terrestrial users with services that are not limited by geographical location. However, the conflict between existing allocation schemes and the business variability between beams is becoming increasingly prominent. Beam hopping technology allows for a more flexible and versatile approach to satellite resource allocation. This paper proposes a beam hopping pattern optimization scheme that jointly considers the interference threshold distance and beam service priority, reducing the inter-beam co-channel interference (CCI). In the cluster area, a non-orthogonal multiple access (NOMA)-based collaborative beam hopping (NCBH) scheme is proposed to minimize the cell-edge user (CEU) interference. Since there is a difference in channel gain between the CEU and cellcenter user (CCU), this scheme forms a NOMA cluster to perform power domain multiplexing and formulates a NOMA cluster pairing strategy according to the user location to reduce the CCI of the CEU. After NOMA cluster pairing, the optimal carrier frequency of the NOMA cluster is selected by a reinforcement learning algorithm. The simulation results verify the excellent performance of the proposed NCBH scheme regarding the user's received power, transmission rate, and outage probability.

A Flexible Topology Reconstruction Strategy Based on Deep Q-Learning for Balance Performance and Efficiency of STINs

Due to the mobility and vulnerability of satellite nodes, the topology of satellite-terrestrial integrated networks (STINs) is highly dynamic. It requires a flexible reconstruction strategy to avoid severe performance loss. However, the current reconstruction strategies usually result in a heavy computational burden, especially in the case of a shortage of satellite resources. Hence, it is of great practical interest to design a flexible topology reconstruction strategy to balance the computational efficiency and network performance of STINs. We investigated this important problem in this paper and made the following contributions. First, a deep Q-learning model is proposed to dynamically optimize the node classification results and flexibly determine the range of network reconstruction, which could enhance the efficiency of the network recovery. Second, a modified SVM classification model is proposed to classify the node type with the hyperplane parameters dynamically adjusted, which can increase the accuracy of the classification result and improve the convergence efficiency of the reconstruction algorithm. Third, an artificial bee colony algorithm is proposed to flexible recover the performance of the damaged network, which considers the age of information and range of the topology cross iteration. Numerical results demonstrate that compared with existing algorithms, the average end-to-end delay decreased by about 13.68%, and other performance indicators of STINs where improved to a certain extent.

Multi-Round Auction-Based Resource Allocation in Multi-Access Edge Computing Assisted Satellite Networks

In this paper, we study the resource allocation problem of multi-access edge computing (MEC) assisted satellite networks in ocean areas. Considering the demand heterogeneity of users and the limited resources of satellites, we propose an online multi-round auction-based resource allocation (OMARA) approach for resource trading between satellites and users, where satellites are the resource sellers and users are the related resource buyers. The proposed approach can effectively determine the price for the trading of resources and can match the service relationships accordingly between satellites and ground users. Finally, it is shown by the simulation experimental results that the proposed approach outperforms the other existing algorithms in maximizing service satisfaction.

Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks.

The observation satellite can exploit its own storage and computational resources to reduce the transmission delay. However, too excessive usage of these resources can have negative effects on the queuing delay at the relay satellite and/or on conducting other tasks at each observation satellite. In this paper, we proposed a new resource- and neighbor-aware observation transmission scheme (RNA-OTS). In RNA-OTS, each observation satellite decides whether to use its resources and the resources of the relay satellite at each time epoch by considering its resource utilization and transmission policies of neighbor observation satellites. For the optimal decision of each observation satellite in a distributed manner, the operation of observation satellites is modeled by means of a constrained stochastic game, and a best-response-dynamics-based algorithm is devised to find the Nash equilibrium. The evaluation results demonstrate that RNA-OTS can decrease the delay to deliver the observation to the destination by up to 87% compared to a relay-satellite-based scheme while guaranteeing a sufficiently low average utilization of the resources of the observation satellite.

Coronal Mass Ejections

The underlying dynamics of solar processes are a topic of considerable interest, because the events on the surface of the Sun directly affect the environment of our planet. Certain ‘storms’ that erupt in the outer layers of the Sun can propel masses of electrically charged plasma into the solar system. If the path of that material crosses Earth’s orbit, it can disrupt our societal electronic and satellite resources. Observing and analyzing these events is a first step toward predicting them and hardening our equipment against them. This work reviews how solar scientists collect and view this data on coronal mass ejections.

Acquisition of Weak BDS Signal in the Lunar Environment

Autonomous navigation using the GNSS has been increasingly used in lunar exploration missions, as it is considered an efficient method for spacecraft to operate without relying on ground facilities. However, so far, only GPS and Galileo have been studied and implemented in lunar missions, whereas the potential of BDS-3 for such missions remains largely unexplored. This paper presents an analysis and evaluation of the navigation service capabilities for spacecraft at lunar altitudes by utilizing existing GNSS satellite resources in orbit. In detail, we investigate the number of GNSS signals received by low lunar orbit (LLO) receivers, as well as the carrier-to-noise ratio (C/N0), Doppler shift, and geometric dilution of precision (GDOP) of the received signal. Additionally, a digital intermediate frequency (IF) signal simulation program to emulate the BDS B1I signal is utilized, which allows to freely set the carrier-to-noise ratio, Doppler shift, and code phase. Based on this framework, we discussed a high-sensitivity BDS B1I signal receiver and validated its performance with simulated signals. We verified the capabilities of coherent integration, non-coherent integration, differential integration, and semi-bit methods to detect weak BDS B1I signals in a lunar environment. The results indicate that the semi-bit coherent differential integration method is still capable of acquiring signals at a C/N0 of 15 dB-Hz and can effectively suppress the navigation data bit sign transition.

Research on requirement fusion of imaging reconnaissance satellites

Aiming at the problem of how to improve the requirement satisfaction of multi-point target imaging reconnaissance tasks and the utilization efficiency of satellite resources, a multi-point target imaging reconnaissance requirement fusion method based on genetic optimization is proposed in this paper. Firstly, the basic constraints that can be fused between multi-point targets are analysed, and on this basis, a point-target synthetic observation model is constructed. Secondly, considering the constraints of observation time window, observation angle and transition time between synthetic tasks, a genetic algorithm-based multi-point target requirements fusion method is designed. Finally, through the simulation analysis, it is concluded that with the continuous increase of user requirements, the fusion method based on genetic optimization can still achieve better task satisfaction to the user’s imaging requirements, which greatly improves the use efficiency of imaging satellites.