Multi-Satellite Scheduling Approach for Dynamic Areal Tasks Triggered by Emergent Disasters

Abstract

ABSTRACT:The process of satellite mission scheduling, which plays a significant role in rapid response to emergent disasters, e.g. earthquake, is used to allocate the observation resources and execution time to a series of imaging tasks by maximizing one or more objectives while satisfying certain given constraints. In practice, the information obtained of disaster situation changes dynamically, which accordingly leads to the dynamic imaging requirement of users. We propose a satellite scheduling model to address dynamic imaging tasks triggered by emergent disasters. The goal of proposed model is to meet the emergency response requirements so as to make an imaging plan to acquire rapid and effective information of affected area. In the model, the reward of the schedule is maximized. To solve the model, we firstly present a dynamic segmenting algorithm to partition area targets. Then the dynamic heuristic algorithm embedding in a greedy criterion is designed to obtain the optimal solution. To evaluate the model, we conduct experimental simulations in the scene of Wenchuan Earthquake. The results show that the simulated imaging plan can schedule satellites to observe a wider scope of target area. We conclude that our satellite scheduling model can optimize the usage of satellite resources so as to obtain images in disaster response in a more timely and efficient manner.

Software-Defined Satellite Observation: A Fast Method Based on Virtual Resource Pools

“…Lemaître et al [22], She et al [30], Chen et al [31], and Frank et al [32] used 0-1 variables to represent the task VTW while indicating the start moment of the task by an integer variable. Niu et al [33] and Chen et al [34] computed nonlinear pose transition time constraints on this basis. Multiple decision models such as these that use 0-1 variables for the visible time window of the satellite performing the task and integer variables for the task start time have better model completeness compared to the previous rule-based decoding process.…”