空间信息网络多平台协同对地观测任务规划方法研究

研究由对地观测卫星、无人机、飞艇和地面站等组成的空间信息网络多平台协同对地观测任务规划问题。综合考虑对地观测和数传平台的资源属性、观测机理、使用约束、以及用户观测需求，通过协同优化方法为各观测任务分配合理的空间信息网络资源并确定执行时间，充分发挥多空间平台的互补优势，提高资源的利用效率和整体观测收益。基于Multi-Agent理论，建立多空间平台协同对地观测任务规划体系结构。基于博弈论，每个空间平台都以最大化整体观测效益为目标，建立协同对地观测任务规划模型。在纳什最优的分布式优化求解框架下，每个空间平台在已知其它平台最优解的前提下，优化自身的目标，进而使整个协同任务规划达到纳什最优。基于合作协同进化思想，设计分布式优化求解算法，对每个空间平台的本地优化问题进行求解。该研究对于丰富空间信息网络资源调度理论和方法，提高空间信息网络资源利用效率，满足多空间平台协同对地观测的军民需求具有重要意义。

We examine multi-platforms cooperation earth observation task planning problem for the Spatial Information Networks (SIN), which is comprised by earth observation satellite, Unmanned Aerial Vehicle (UAV), airship, ground station, and other platforms. With considering earth observation and data transmission platform resource attribute, observation mechanism, use constraint, and observation requirements, SIN resource are distributed, and execution time are determined by cooperative optimization method for each observation task, so to obtain complementary advantages for spatial platforms, and resource utilization efficiency and overall observing income are enhanced. We construct multi-platforms cooperation earth observation task planning architecture by Multi-Agent theory. With the same target of overall observing income for each platform, we construct multi-platforms cooperation earth observation task planning model based on game theory. Under the Nash optimal multi-platforms distributed optimization framework, each platform optimize own objective on the premise of other platforms’ optimum solution, so as to realize Nash optimal for the whole cooperation task planning. Distributed optimization algorithm is proposed to solve local optimization problem for each spatial platform based on cooperative co-evolution. This study has great significance to enrich SIN resource scheduling theory and methods, enhance SIN resource utilization efficiency, and satisfy the multi-platforms cooperation earth observation military and civilian demand.