A Genetic Algorithm For Satellite Observation And Data Download Integrated Scheduling

With the continuous development of satellite technology,satellite plays an increasingly important role in the military and civil applications.Then,with the rapid growth of user demand,satellite resource growth has been unable to meet the growth of user demand.So,it is very much need for satellite mission to be scheduled to maximize meet the needs of users.The complete satellite mission scheduling involves planning the observation of the ground target and transmitting the image to the ground station.Therefore,this paper is aimed at satellite integrated scheduling of observation and download.Earth observation satellite scheduling considers two types of tasks: Earth observation and data download.The Earth observation is predetermined according to user requirements before scheduling,while data download is a real-time task generated in the process of scheduling.Integrating two kinds of tasks,to a large extent,increases the difficulty of scheduling.In this article the integrated scheduling problem is analyzed.We establish an integer programming model with storage and energy constraints,which is NP-hard in computational complexity.We develop a double chromosome genetic algorithm to solve the model.One chromosome is used to represent the task sequence and the other chromosome to record the storage and energy consumption of EOSs.A crossover operator based on switching time window and a mutation operator based on time window recombination are designed.Simulation results show that the proposed algorithm outpeforms classical genetic algorithm and other two meta-heuristics in terms of schedule quality and running time.The results of the research can effectively use the satellite resource to observe the mission as many as possible,which is of great practical significance to solve the problem of satellite observation and data download integrated scheduling.