The Low Earth Orbit(LEO)remote sensing satellite mega-constellation has the characteristics of large quantity and various types which make it have unique superiority in the realization of concurrent multiple tasks.How...The Low Earth Orbit(LEO)remote sensing satellite mega-constellation has the characteristics of large quantity and various types which make it have unique superiority in the realization of concurrent multiple tasks.However,the complexity of resource allocation is increased because of the large number of tasks and satellites.Therefore,the primary problem of implementing concurrent multiple tasks via LEO mega-constellation is to pre-process tasks and observation re-sources.To address the challenge,we propose a pre-processing algorithm for the mega-constellation based on highly Dynamic Spatio-Temporal Grids(DSTG).In the first stage,this paper describes the management model of mega-constellation and the multiple tasks.Then,the coding method of DSTG is proposed,based on which the description of complex mega-constellation observation resources is realized.In the third part,the DSTG algorithm is used to realize the processing of concurrent multiple tasks at multiple levels,such as task space attribute,time attribute and grid task importance evaluation.Finally,the simulation result of the proposed method in the case of constellation has been given to verify the effectiveness of concurrent multi-task pre-processing based on DSTG.The autonomous processing process of task decomposition and task fusion and mapping to grids,and the convenient indexing process of time window are verified.展开更多
During the execution of imaging tasks,satellites are often required to observe natural disasters,local wars,and other emergencies,which regularly interferes with the execution of existing schemes.Thus,rapid satellite ...During the execution of imaging tasks,satellites are often required to observe natural disasters,local wars,and other emergencies,which regularly interferes with the execution of existing schemes.Thus,rapid satellite scheduling is urgently needed.As a new generation of three degree-of-freedom(roll,pitch,and yaw)satellites,agile earth observation satellites(AEOSs)have longer variable-pitch visible time windows for ground targets and are capable of observing at any time within the time windows.Thus,they are very suitable for emergency tasks.However,current task scheduling models and algorithms ignore the time,storage and energy consumed by pitch.Thus,these cannot make full use of the AEOS capabilities to optimize the scheduling for emergency tasks.In this study,we present a fine scheduling model and algorithm to realize the AEOS scheduling for emergency tasks.First,a novel time window division method is proposed to convert a variable-pitch visible time window to multiple fixed-pitch visible time windows.Second,a model that considers flexible pitch and roll capabilities is designed.Finally,a scheduling algorithm based on merging insertion,direct insertion,shifting insertion,deleting insertion,and reinsertion strategies is proposed to solve conflicting problems quickly.To verify the effectiveness of the algorithm,48 groups of comparative experiments are carried out.The experimental results show that the model and algorithm can improve the emergency task completion efficiency of AEOSs and reduce the disturbance measure of the scheme.Furthermore,the proposed method can support hybrid satellite resource scheduling for emergency tasks.展开更多
传统模式下,卫星采取单任务观测方式,该种方式下任务的成像精度高但任务成像数量少且资源使用率极低。因此,在单任务观测方式的基础上设计了一种多任务合成机制(multi-task merging mechanism,MTMM),在保证用户最低成像要求的情况下对...传统模式下,卫星采取单任务观测方式,该种方式下任务的成像精度高但任务成像数量少且资源使用率极低。因此,在单任务观测方式的基础上设计了一种多任务合成机制(multi-task merging mechanism,MTMM),在保证用户最低成像要求的情况下对任务合成。首先,基于合成任务集,建立多星调度模型。然后,针对模型提出了基于任务合成的改进蚁群优化(improved ant colony optimization based on task merging,IACO-TM)算法,在算法中设计了自适应蚁窗策略、强制扰动机制以及算法参数动态调节策略,对蚂蚁搜索空间进行有效裁剪,避免算法陷入局部最优的同时提高算法的收敛速度。最后,通过大量仿真实验与不考虑任务合成的改进蚁群优化(improved ant colony optimization,IACO)算法和基于任务合成的传统蚁群优化(traditional ant colony optimization based on task merging,TACO-TM)算法对比,验证了所提MTMM和IACO-TM的有效性。展开更多
基金supported by the National Natural Science Foundation of China(Nos.62003115 and 11972130)the Shenzhen Science and Technology Program,China(JCYJ20220818102207015)the Heilongjiang Touyan Team Program,China。
文摘The Low Earth Orbit(LEO)remote sensing satellite mega-constellation has the characteristics of large quantity and various types which make it have unique superiority in the realization of concurrent multiple tasks.However,the complexity of resource allocation is increased because of the large number of tasks and satellites.Therefore,the primary problem of implementing concurrent multiple tasks via LEO mega-constellation is to pre-process tasks and observation re-sources.To address the challenge,we propose a pre-processing algorithm for the mega-constellation based on highly Dynamic Spatio-Temporal Grids(DSTG).In the first stage,this paper describes the management model of mega-constellation and the multiple tasks.Then,the coding method of DSTG is proposed,based on which the description of complex mega-constellation observation resources is realized.In the third part,the DSTG algorithm is used to realize the processing of concurrent multiple tasks at multiple levels,such as task space attribute,time attribute and grid task importance evaluation.Finally,the simulation result of the proposed method in the case of constellation has been given to verify the effectiveness of concurrent multi-task pre-processing based on DSTG.The autonomous processing process of task decomposition and task fusion and mapping to grids,and the convenient indexing process of time window are verified.
基金supported by the National Natural Science Foundation of China under Grant Nos.72071064 and 71521001.
文摘During the execution of imaging tasks,satellites are often required to observe natural disasters,local wars,and other emergencies,which regularly interferes with the execution of existing schemes.Thus,rapid satellite scheduling is urgently needed.As a new generation of three degree-of-freedom(roll,pitch,and yaw)satellites,agile earth observation satellites(AEOSs)have longer variable-pitch visible time windows for ground targets and are capable of observing at any time within the time windows.Thus,they are very suitable for emergency tasks.However,current task scheduling models and algorithms ignore the time,storage and energy consumed by pitch.Thus,these cannot make full use of the AEOS capabilities to optimize the scheduling for emergency tasks.In this study,we present a fine scheduling model and algorithm to realize the AEOS scheduling for emergency tasks.First,a novel time window division method is proposed to convert a variable-pitch visible time window to multiple fixed-pitch visible time windows.Second,a model that considers flexible pitch and roll capabilities is designed.Finally,a scheduling algorithm based on merging insertion,direct insertion,shifting insertion,deleting insertion,and reinsertion strategies is proposed to solve conflicting problems quickly.To verify the effectiveness of the algorithm,48 groups of comparative experiments are carried out.The experimental results show that the model and algorithm can improve the emergency task completion efficiency of AEOSs and reduce the disturbance measure of the scheme.Furthermore,the proposed method can support hybrid satellite resource scheduling for emergency tasks.
文摘传统模式下,卫星采取单任务观测方式,该种方式下任务的成像精度高但任务成像数量少且资源使用率极低。因此,在单任务观测方式的基础上设计了一种多任务合成机制(multi-task merging mechanism,MTMM),在保证用户最低成像要求的情况下对任务合成。首先,基于合成任务集,建立多星调度模型。然后,针对模型提出了基于任务合成的改进蚁群优化(improved ant colony optimization based on task merging,IACO-TM)算法,在算法中设计了自适应蚁窗策略、强制扰动机制以及算法参数动态调节策略,对蚂蚁搜索空间进行有效裁剪,避免算法陷入局部最优的同时提高算法的收敛速度。最后,通过大量仿真实验与不考虑任务合成的改进蚁群优化(improved ant colony optimization,IACO)算法和基于任务合成的传统蚁群优化(traditional ant colony optimization based on task merging,TACO-TM)算法对比,验证了所提MTMM和IACO-TM的有效性。
