Research On The Shortest Time Multiple Satellite Scheduling Optimization Method

The shortest time multiple satellite scheduling optimization method refers to making use of multiple satellites for task planning when satellite resources are sufficient,formulating the optimal coverage scheme for rational allocation of satellite resources to ensure mutual cooperation between satellites and completely cover the regional target in the shortest time,efficiently accomplish image data collection of the regional target.The shortest time multiple satellite scheduling is often applied to emergency observation tasks,especially in the process of responding to natural disasters and emergencies.During the golden rescue period,the shortest time multiple satellite scheduling is carried out according to the time and circumstances,and the satellite observation task is completed as soon as possible.It is of great significance to protect the safety of people’s lives.At the same time,it can also minimize the losses caused by disasters and reduce the impact of accidents.In this paper,the shortest time multiple satellite scheduling optimization method is studied.Based on the mesh discretization method,the regional target is dynamically decomposed,and a multiple satellite scheduling model of the shortest time completely cover regional target is established.Then a two-step algorithm is designed to optimize the model:The first step is to apply the heuristic algorithm on a large size double mesh to quickly find a coverage scheme for completely cover regional target by increasing coverage opportunities by time period.The second step is to gradually delete the coverage opportunities according to the end time and optimize the coverage completion time.When optimizing at each moment,the heuristic algorithm is applied on a small size double mesh to find a coverage scheme for regional target area maximization,and then the approximation algorithm is applied to improve the coverage efficiency and optimize the coverage scheme.We propose two algorithms to increase coverage opportunities by time period and three shortest time optimization algorithms to delete coverage opportunities according to the end time.Finally,the algorithms are simulated and compared.