Satellite Mission Planning Research Articles

Spatio-temporal grid-based storage method for aerospace operation and control data

Abstract The current aerospace operation and control data system has problems such as difficult integration organization and difficult data retrieval in terms of mission planning efficiency, which limits the development of satellite operation and control systems. This paper is oriented to the organization and management of aerospace operation and control data, according to the nature of the current big data indexing model. Based on the principle of earth dissection, we aim to design a grid that can be indexed. First of all, the Earth is dissected to form a hierarchical and uniformly distributed global geographic grid, and then the formed grid is encoded to store satellite attribute information and orbit data for mission planning. Finally, through the data retrieval experiment, the retrieval time is within 100 milliseconds under ten million pieces of data, which proves that the spatial-temporal grid model has the characteristics of efficient and fast retrieval. It can provide the basic information for satellite mission planning to further improve the efficiency of satellite mission planning and management and the efficiency and quality of satellite mission planning.

On Lyapunov stability of artificial potential function-based low-thrust constellation reconfiguration control

The configuration control of constellations which consist of numerous satellites presents significant challenges. Such space systems demand the capability for autonomous reconfiguration to accomplish specific missions. Individual satellites within constellations are primarily equipped with low-thrust systems, rendering traditional impulse control methods for satellite formation and mission planning ineffective. To address this issue, this paper proposes a low-thrust constellations reconfiguration control method utilizing an artificial potential function. Relying exclusively on neighboring state information, satellites can dynamically modulate their control efforts in real-time. The stability of the proposed reconfiguration control method is established under modelable, bounded perturbation using Lyapunov theory. Furthermore, a repulsive potential function is formulated to ensure collision avoidance among satellites. Numerical results indicate that the proposed method is stable, computationally efficient, and guarantees the safe distance during maneuvers. Consequently, this method is ideally suited for reconfiguration tasks across a spectrum of large space systems, including mega-constellations.

Quantum Optimization Methods for Satellite Mission Planning

Quantum Optimization Methods for Satellite Mission Planning

Multi-Objective Multi-Satellite Imaging Mission Planning Algorithm for Regional Mapping Based on Deep Reinforcement Learning

Satellite imaging mission planning is used to optimize satellites to obtain target images efficiently. Many evolutionary algorithms (EAs) have been proposed for satellite mission planning. EAs typically require evolutionary parameters, such as the crossover and mutation rates. The performance of EAs is considerably affected by parameter setting. However, most parameter configuration methods of the current EAs are artificially set and lack the overall consideration of multiple parameters. Thus, parameter configuration becomes suboptimal and EAs cannot be effectively utilized. To obtain satisfactory optimization results, the EA comp ensates by extending the evolutionary generation or improving the evolutionary strategy, but it significantly increases the computational consumption. In this study, a multi-objective learning evolutionary algorithm (MOLEA) was proposed to solve the optimal configuration problem of multiple evolutionary parameters and used to solve effective imaging satellite task planning for region mapping. In the MOLEA, population state encoding provided comprehensive population information on the configuration of evolutionary parameters. The evolutionary parameters of each generation were configured autonomously through deep reinforcement learning (DRL), enabling each generation of parameters to gain the best evolutionary benefits for future evolution. Furthermore, the HV of the multi-objective evolutionary algorithm (MOEA) was used to guide reinforcement learning. The superiority of the proposed MOLEA was verified by comparing the optimization performance, stability, and running time of the MOLEA with existing multi-objective optimization algorithms by using four satellites to image two regions of Hubei and Congo (K). The experimental results showed that the optimization performance of the MOLEA was significantly improved, and better imaging satellite task planning solutions were obtained.

Multiple super-agile satellite collaborative mission planning for area target imaging

Collaboration between multiple super-agile satellites with dynamic imaging capabilities provides an opportunity for highly efficient observation of large-area targets. Reasonable allocation of multi-satellite resources and determination of the action sequences of each satellite are two key requirements for multiple super-agile satellite mission planning. We propose a multi-satellite collaborative mission planning approach for an area target imaging mission, considering the dynamic imaging characteristics, achieving integrated optimization of the allocation of multi-satellite resources and determination of the action sequences of each satellite. A multi-satellite mission planning model with two objectives was established, with the objective functions being to maximize the observation coverage revenue and minimize the task performance time. An improved particle swarm optimization algorithm, represented by introducing particle learning/selection strategies and variable type-adaptive position updating methods, addresses the discrete–continuous hybrid variables in this optimization model. The proposed method was verified by constructing three groups of comparative experiments with area targets of different sizes. The proposed method showed consistent adaptability to various complexities and achieved synchronous optimization of the allocation of multi-satellite resources and determination of the action sequence of each satellite in areas of different sizes. Compared with existing algorithms, the proposed algorithm obtained superior imaging mission schemes with lower computational consumption, higher observation coverage revenues, or shorter task performance time. The experimental results showed that the proposed multi-satellite collaborative mission planning approach could be used to the ground operational systems of super-agile satellite constellations.

Multi-satellite Mission Planning Algorithm Based on Preemptive Priority Model

<p>Aiming at the problems of high time-consuming and insufficient guarantee of high-priority task completion in the large-scale multi-satellite multi-observation mission planning application, a multi-satellite multi-mission planning model based on preemptive priority model is proposed. Combined with the whole-neighborhood greedy search strategy, an improved tabu search algorithm is designed, taking the combination of satellite observation windows as the decision variables. The designed algorithm realizes an efficient solver to the proposed mission planning model. The simulation results show that the average solving time of the algorithm is 144s under the problem scale of 100 satellites and 3000 tasks. Compared with the existing algorithm, the calculation time is shortened by at least 75% under the premise that the total number of planning tasks is comparable, which is more in line with the high time efficiency requirements for satellite mission planning in engineering applications.</p> <p> </p>

A Hybrid Discrete Artificial Bee Colony Algorithm for Imaging Satellite Mission Planning

Imaging satellite mission planning has received more and more attention as one of the core problems in the field of imaging satellite applications. In this paper, a hybrid discrete artificial bee colony (HDABC) algorithm is proposed to address this problem. The HDABC algorithm improves the three search phases of the basic artificial bee colony (ABC) algorithm to make them applicable to the discrete satellite mission planning problem. In the employed bee search phase, the population is divided and a multi-strategy search equation mechanism is used to balance the exploration and development of the algorithm. In the following bee search phase, two kinds of neighborhood search operators are designed based on the problem characteristics to further improve the fitness values of the better solutions. In the scout bee search phase, a migration operator and an immigration operator are introduced to improve the fitness values of the worse solutions and promote the exchange of different subpopulations to achieve co-evolution. In the experimental part, orthogonal experimental design is used to determine the appropriate algorithm parameters. Simulation experiments are carried out to test problems of different sizes. The experimental results show that the proposed HDABC algorithm shows good performance.

Distributed Imaging Satellite Mission Planning Based on Multi-Agent

Distributed Imaging Satellite Mission Planning Based on Multi-Agent

Quantum Algorithms Applied to Satellite Mission Planning for Earth Observation

Quantum Algorithms Applied to Satellite Mission Planning for Earth Observation

A Review on Machine Learning for Earth Observation Satellite Mission Planning

: Mission planning is an integral process that enables earth observation missions and defines their success rate. This paper identifies key problems and opportunities in machine learning for earth observation satellite mission planning. Firstly, the description elements, classification methods, solution process and solution difficulties of the mission planning problem of earth observation satellite are described. Secondly, the current research status of machine learning for the earth observation satellite mission planning is summarized and analyzed. Finally, the problems of current research are analyzed, and the prospect of new field research is given in light of the development needs.

A Region Preference-based Optimization Algorithm for Satellite Mission Planning Problem

For the earth observation satellite mission planning problem, objectives such as observed target quantity, observation profit, energy consumption, image quality should be considered simultaneously, which is a many-objective optimization problem. Classical optimization-based mission planning algorithms obtain a set of non-dominated solutions in the entire search space, while only a single satisfy final plan is desired by decision maker. In this paper, a five-objective optimization model for satellite mission planning problem is constructed, then a region preference-based evolutionary algorithm, HMOEA-T, is applied to obtain the desired solutions. The decision makers describe the preference on each objective in target region form, then the algorithm guides a more detailed search within the preference region rather than the entire Pareto front. Comparative studies with preference-based methods (T-NSGA-III) and classical methods (NSGA-III) are conducted. We have exemplified the proposed method manage to obtain the solutions satisfying the mission planning preference and achieve better performance in convergence and diversity.

Dual-Population Artificial Bee Colony Algorithm for Joint Observation Satellite Mission Planning Problem

Earth observation satellites provide services for users by taking images. The rapid increase in the number of satellites and missions makes mission planning more difficult. In order to solve this problem, this paper adopts a multi-population evolutionary algorithm. First, a mixed-integer programming model of the joint observation satellites mission planning problem (JOSMPP) is constructed. After that, a dual-population artificial bee colony algorithm (DPABC) and a heuristic task scheduling algorithm are proposed. Two learning-based nectar generation methods are used to guide the direction of population optimization. The search population is used to lead the nectar search at the stage of employed bees and supplement population is used to improve search performance. According to the performance of these two populations, candidate populations are generated to realize the search of onlooker bees. After each generation of population search, the composition of the two populations is adjusted according to the performance. Finally, the experimental results show that the DPABC algorithm has more advantages than multiple state-of-the-art algorithms to solve the joint mission planning problem of earth observation satellites.

Mission Planning Issues of Imaging Satellites: Summary, Discussion, and Prospects

Satellite mission planning is the basis and top-level work of space missions and the beginning of each space mission. Therefore, the scientific research of satellite mission planning is very important. By analyzing the existing research results, we can know that the research on task planning mainly focuses on three aspects: research objects, established model, and solution algorithm. Starting from these three aspects vertically and then horizontally, this paper comprehensively discusses the theoretical basis, application, and advantages and disadvantages of related technologies in the research literature in recent years. Finally, based on the research on satellite mission planning, this paper puts forward its own views on the future development direction and research focus.

Design and Development of High-Performance Intelligent Disaster Management System in IoT Paradigm A Novel Architecture and Review on Challenges in real time Hazards Monitoring

In recent years India is suffering from various natural disaster which have great effect on social life of people and large burden on disaster management authority. The high frequency disaster causes a serious loss of property, life and present more complex damage. Disaster management involves the detailed process of disaster response. A High-Performance Intelligent Disaster Management System can realize the complete disaster avoidance and reduction from satellite mission planning, data production, data acquisition, with the application of remote sensing and managing integrated rapid service. The main objective of proposed work is to overcome the limitations of disaster management with a novel design and development of IoT based platform for the application of disaster management system.

Service Chaining Placement Based on Satellite Mission Planning in Ground Station Networks

As the increase in satellite number and variety, satellite ground stations should be required to offer user services in a flexible and efficient manner. Network function virtualization (NFV) can provide a new paradigm to allocate network resources on-demand for user services over the underlying network. However, most of the existing work focuses on the virtual network function (VNF) placement and routing traffic problem for enterprise data center networks, the issue needs to further study in satellite communication scenarios. In this article, we investigate the VNF placement and routing traffic problem in satellite ground station networks. We formulate the problem of resource allocation as an integer nonlinear programming (INLP) model and the objective is to minimize the link resource utilization and the number of servers used. Considering the information about satellite orbit fixation and mission planning, we propose location-aware resource allocation (LARA) algorithms based on Greedy and IBM CPLEX 12.10, respectively. The proposed LARA algorithm can assist in deploying VNFs and routing traffic flows by predicting the running conditions of user services. We evaluate the performance of our proposed LARA algorithm in three networks of Fat-Tree, BCube, and VL2. Simulation results show that our proposed LARA algorithm performs better than that without prediction, and can effectively decrease the average resource utilization of satellite ground station networks.

Advancing Learning Assignments in Remote Sensing of the Environment Through Simulation Games

Environmental remote sensing has faced increasing satellite data availability, advanced algorithms for thematic analysis, and novel concepts of ground truth. For that reason, contents and concepts of learning and teaching remote sensing are constantly evolving. This eventually leads to the intuition of methodologically linking academic learning assignments with case-related scopes of application. In order to render case-related learning possible, smart teaching and interactive learning contexts are appreciated and required for remote sensing. That is due to the fact that those contexts are considered promising to trigger and gradually foster students’ comprehensive interdisciplinary thinking. To this end, the following contribution introduces the case-related concept of applying simulation games as a promising didactic format in teaching/learning assignments of remote sensing. As to methodology, participating students have been invited to take on individual roles bound to technology-related profiles (e.g., satellite-mission planning, irrigation, etc.) Based on the scenario, stakeholder teams have been requested to elaborate, analyze and negotiate viable solutions for soil moisture monitoring in a defined context. Collaboration has been encouraged by providing the protected, specifically designed remoSSoil-incubator environment. This letter-type paper aims to introduce the simulation game technique in the context of remote sensing as a type of scholarly teaching; it evaluates learning outcomes by adopting certain techniques of scholarship of teaching and learning (SoTL); and it provides food for thought of replicating, adapting and enhancing simulation games as an innovative, disruptive next-generation learning environment in remote sensing.

Research Progress on Requirements Integrated Preprocessing and Mission Planning for Earth Observation Satellites

The scale of Earth observation satellite system continues to expand and its application is becoming more and more extensive, which puts forward higher requirements for satellite mission planning system. This paper summarizes and analyzes some key issues in the mission planning of earth observation satellites. Firstly, the development status and the trend of earth observation satellites are introduced. Secondly, we summarize and analyze complex requirements of observation decomposition method, the priority of requirements determination method, the model construction and solving methods of single-satellite scheduling, multi-satellite integrated scheduling and multi-satellite dynamic scheduling in the process of requirements Integrated Preprocessing and mission planning. Finally, we discuss the problems that still need to be solved and the direction of research.

Learning-guided nondominated sorting genetic algorithm II for multi-objective satellite range scheduling problem

Abstract Satellite range scheduling is an important issue in the field of satellite mission planning, which greatly affects the development of satellite industry. This paper analyzed satellite range scheduling problem, constructed a multi-objective SRSP (MO-SRSP) model and proposed an improved multi-objective evolutionary algorithm (MOEA), called learning-guided nondominated sorting genetic algorithm II (LGNSGAII) that contains a learning mechanism. Learning mechanisms can speed up optimization process. Meanwhile, another algorithm called task-time window selection algorithm (TTSA) is also proposed. Specifically, it can select satellite ground stations time windows for tasks. TTSA includes three location selection methods and two location movement methods, both are used to select the appropriate execution location for tasks. Experiments show that the proposed algorithm can solve MO-SRSP better than several comparison algorithms. In other words, this algorithm we proposed has a broad practical application prospect.

Optimisation problems and resolution methods in satellite scheduling and space-craft operation: a survey

ABSTRACT The fast development in the production of small, low-cost satellites is propelling an important increase in satellite mission planning and operations projects. Central to satellite mission planning is the resolution of scheduling problem for an optimised allocation of user requests for efficient communication between operations teams at the ground and spacecraft systems. The aim of this paper is to survey the state of the art in the satellite scheduling problem, analyse its mathematical formulations, examine its multi-objective nature and resolution through meta-heuristics methods. Finally, we consider some optimisation problems arising in spacecraft design, operation and satellite deployment systems.

A framework involving MEC: imaging satellites mission planning

Satellite will play an important role in many important industries and exist as a carrier of information transmission in the era of Internet of Things. Massive data can be used in planning and scheduling processes A general data-driven framework-imaging satellite mission planning framework (ISMPF) for solving imaging mission planning problems is proposed. ISMPF mainly includes three parts: task assignment, planning and scheduling and task execution. The framework gives a general solution to the problem of satellite mission planning. The two core parts of the planning and scheduling module are machine learning algorithms and planning and scheduling algorithms, which greatly affect the quality of the results. Machine learning algorithm is mainly used to quickly obtain feasible initial solution. This idea can be used to quickly analyze and model the imaging satellite observation mission planning, imaging satellite measurement and control, data downlink mission planning problems. It has a strong generality and is suitable for most situations of imaging satellites. In order to verify the validity of ISMPF, we designed test examples for measurement and control, data downlink missions. Experimental verification demonstrates the effectiveness of our proposed framework.