Research On Single Satellite Mission Planning Algorithm Of Agile Satellite

Agile satellite’s flexible mobility enables it to observe more complex tasks.While the observing ability improves,the study of agile satellite mission planning has also become more complicated and difficult.How to give full play to the flexible mobility of agile satellites and improve the efficiency of satellite observation has become a hot issue in recent years.This paper takes the mission planning process of single agile satellite observing regional targets as the research object,starting from two aspects:non track decomposition of regional targets and determination of task start observation time,The mission planning and scheduling software applied in engineering practice is improved and optimized to improve the efficiency of mission planning and observation.And based on the development trend of satellite intelligence and autonomy,a new autonomous mission planning mode is proposed.Firstly,the agile satellite mission planning problem model is established.By analyzing the working principle and execution process of agile satellite earth observation,this paper expounds the necessity of studying agile satellite mission planning and the causes of related constraints.Discussed the input and output elements of the agile satellite mission planning problem,and the reasonable assumptions and related simplification of the problem are carried out.On this basis,an agile satellite mission planning model with maximizing mission revenue and minimizing imaging maneuver angle as the objective function is established.Secondly,a non tracking decomposition algorithm of regional targets based on particle swarm optimization algorithm is designed.The appropriate division angle can reduce the number of bands and shorten the observation time of the task.After designing the solution scheme of the non trace decomposition of the regional target,this paper uses the particle swarm optimization algorithm to search the best division angle of the non trace decomposition of the regional target.The simulation results show that the decomposition method can significantly reduce the number of bands in regional decomposition and greatly shorten the time of regional observation compared with the trace decomposition method.While the non trace decomposition method based on particle swarm optimization algorithm can reduce the number of divided bands and the total length of bands by 11.52%compared with the non trace decomposition method based on the history method when the running time of the algorithm is reduced by 3.35%,The time of regional observation is reduced by 8.49%,which improves the observation efficiency of satellites.Then,an agile satellite mission planning algorithm based on genetic algorithm and multilevel sorting rules is proposed.In the existing research,there is little research on the start observation time of the mission.The appropriate observation time can reduce the imaging maneuver angle of the satellite,reduce resource consumption,and improve the imaging quality.Firstly,the improved genetic algorithm is used to optimize the start observation time of the task,and then the observation sequence of the task is determined based on the multi-level sorting rules.When there is observation time conflict in the observation sequence,the window sliding method is used to eliminate the conflict.Compared with the task planning method used in engineering practice,the observation time planned by the algorithm is reduced by 5.08%,the observation income is improved,and the imaging maneuver angle is reduced by 7.25%.Finally,an agile satellite autonomous mission planning method based on the best observation scheme is proposed.In order to improve satellite autonomy and reduce the complexity of task planning process,the optimal observation scheme for providing a single task on the ground and the Autonomous Task Planning Mode for determining the overall observation scheme on the satellite according to the actual satellite and environmental state and the queue to be observed are proposed.Aiming at the problem of providing the best observation scheme for a single task on the ground,on the premise of optimal energy and taking into account the task observation time,a task processing method of CO optimizing the area division angle and the task start observation time is proposed,and a particle swarm immune tabu search algorithm is designed to solve the problem.Compared with the angle and time separated optimization algorithm designed in this paper,the imaging maneuver angle of this method is reduced by 7.25%,which proves the feasibility and effectiveness of collaborative optimization,and points out their respective application scenarios.