Research On Multiple Satellite Mission Planning Methods For Large Regional Target Observation

With the rapid development of aerospace technology,the demand of satellite observation information has become more diversified.In actual engineering applications,engineers sometimes encounter some tasks of observing large region,and the target area needs to be covered to obtain effective information.Due to the limitation of high-resolution agile satellite scanning width and the number of satellite transits in a short time,a single satellite cannot meet the timeliness requirements of the observation mission,and multiple satellites are required to coordinate to achieve effective observation of the regional target.At present,in the multi-satellite mission planning research for large-area observations,most of the satellites involved are swing satellites,which are not suitable for agile satellites.Therefore,how to use multiple agile satellites to achieve large-area collaborative observations still needs further research.This paper takes the multiple satellites mission planning for large regional target observation as the research object,studies the regional target decomposition method in the preprocessing stage,and designs the regional target decomposition based on the satellite maneuverability.In order to solve the allocation problem of multiple agile satellites after regional target decomposition,two algorithms are proposed.They are a multiple satellites mission planning algorithm based on a divide-and-conquer strategy with a single optimization objective,and the other is a multiple satellites mission planning algorithm based on multiple optimization objectives.The effectiveness of the two proposed algorithms is verified by simulation.First,the process and characteristics of agile satellite mission planning are introduced.Analyze the constraints and optimization objectives involved in the multiple satellites mission planning problem for large regional target observation.Give solutions to the problem of multiple satellites mission planning for large-area target observations.Secondly,the area decomposition method in the preprocessing stage is studied,and a dynamic decomposition algorithm based on satellite maneuverability is proposed.According to the characteristics of large-area collaborative observation of multiple agile satellites,a grid discrete target area is established.Combined with the characteristics of the satellite’s maneuverability and visible time window,the grid is used to construct optional observation strips for each transit observation of the satellite to complete the observation.The decomposition of the target area;the simulation results show that the proposed algorithm has a coverage rate improvement of about 12%compared with the area decomposition algorithm with discrete roll angle.Then,a single-objective multi-satellite mission planning algorithm based on divide-and-conquer strategy is designed.Aiming at the satellite allocation problem of multiple agile satellites after large area decomposition under multiple transits,a hierarchical framework is designed to decompose the multi-satellite mission planning problem and simplify it into imaging mission allocation and single-orbit mission planning.The single-objective optimization model is based on a single-objective optimization model;the improved artificial bee colony algorithm is used to assign imaging strips,and the simulated annealing algorithm is used for single-track mission planning;the mission planning schemes of all transit orbits are combined for analysis and evaluation,and the results are fed back to the imaging task assignment.According to the feedback information,the imaging task assignment is performed again,and the final optimization result is obtained;the simulation results show that the proposed algorithm is about 3.5%higher than the genetic algorithm,and about 5.5%higher than the tabu search algorithm.,which can realize the rapid solution of multi-satellite mission planning problems,and show excellent performance in coverage yield and time efficiency.Finally,a multi-satellite mission planning algorithm based on multi-objective optimization is designed.In order to improve the quality of the solution,a multi-objective mission planning model was established with the maximum coverage and the minimum number of imaging strips as the optimization goals,and a multi-satellite mission planning algorithm based on the improved NSGA-Ⅱ was designed to solve the model.The simulation results show that the proposed Compared with the genetic algorithm,the coverage rate of the algorithm is increased by about 4%.The increase of the coverage rate does not take a lot of time,and a better solution can be obtained within a certain period of time.Through comparison and simulation verification,the two proposed algorithms can effectively complete the multi-satellite mission planning solution for large-area observations.