遥感技术与应用,2023,38(4):783-793doi:10.11873/j.issn.1004-0323.2023.4.0783
宽波段多光谱数据立方专栏
2.中国科学院长春光学精密机械与物理研究所，吉林长春130033
3.中国科学院大学，北京100049
2.ChangchunInstituteofOptics，FineMechanicsandPhysics，ChineseAcademyofSciences，Changchun130033，China
3.UniversityofChineseAcademyofSciences，Beijing100049，China
收稿日期:2022-08-25修回日期:2023-06-25
Received:2022-08-25Revised:2023-06-25
作者简介Aboutauthors
关键词：地球观测卫星;区域分解;卫星调度与规划;地面站;多目标进化算法;多星协同
Keywords：Earthobservationsatellite;Regionaldecomposition;Satelliteschedulingandplanning;Groundstation;Multi-objectiveevolutionaryalgorithm;Multi-satellitecoordination
本文引用格式
何奇恩,李峰,钟兴.多目标算法在卫星区域覆盖调度及数传规划上的应用综述.遥感技术与应用[J],2023,38(4):783-793doi:10.11873/j.issn.1004-0323.2023.4.0783
HEQi'en,LIFeng,ZHONGXing.AReviewoftheApplicationofMulti-objectiveAlgorithmsinSatelliteRegionalCoverageSchedulingandDataTransmissionPlanning.RemoteSensingTechnologyandApplication[J],2023,38(4):783-793doi:10.11873/j.issn.1004-0323.2023.4.0783
图1卫星数据获取流程图
Fig.1Flowchartofsatellitedataacquisition
图2点目标、区域目标及区域覆盖示意图
Fig.2Schematicdiagramofspottarget，areatargetandregionalcoverage
网格法是经典的区域分解方法之一，按网格属性又可细分为等经纬度法和等面积法。
图3等经纬度分解法
Fig.3Equallongitudeandlatitudedecompositionmethod
图4等面积分解法
Fig.4Equalareadecompositionmethod
图5条带分解法
Fig.5Stripdecompositionmethod
条带法不考虑所生成条带的长度以及如何最优化所覆盖网格，只通过调整侧摆角步长来控制生成条带的数量。因此与网格法相比，该方法可以明显降低候选条带的数量，减少冗余条带对后续调度模型求解速度的影响。另一方面，在得到区域覆盖计划之后通常需要对条带长度进行剪裁，以避免不必要的资源浪费。
图6多目标进化算法求解示意图
Fig.6Solutionsofmulti-objectiveevolutionaryalgorithm
表1变量及说明
Table1Variablesandexplanation
决策变量：
（1）是否在观测任务中选择某条带：
常见目标函数：
（1）最大化覆盖面积（收益）：
（2）最小化所选条带个数：
（3）最小化条带重叠率：
（5）最大化数传时长：
（6）最大化数传任务完成率：
常见约束条件：
（1）每颗卫星每次过境只能选择一个条带用于观测：
（2）每颗卫星的内存限制：
（4）获得的图像分辨率满足所要求分辨率：
（5）侧摆角度在允许范围内：
（6）每个地面站每次只能与一颗卫星进行数传：
解决多星协同覆盖区域目标的一般方法包括两个步骤。第一步是将目标区域按照一定的规则分解成一系列元任务（meta-tasks），即区域分解过程。第二步是设计各种模型和算法（多为多目标进化算法）选择这些元任务的一个子集，并将它们分配给特定的卫星。换言之，该方法将卫星区域覆盖问题分为两个子问题，即目标区域的分解和调度模型的优化。
图7加入目标区域偏好信息的多目标进化算法求解示意图
Fig.7Solutionsofmulti-objectiveevolutionaryalgorithmwithtargetregionpreferenceinformation
（1）点目标和多边形区域目标的联合观测任务规划。实际的用户观测需求往往是点目标和区域目标的结合。现有的区域分解和调度、规划方法不能很好地解决两者同时观测的问题。今后应重点研究融合点目标和区域目标的多星多目标协同观测问题。
（3）将多目标算法与其他方法相融合。多目标算法种类繁多，特点各异。针对其自身局限性，可将其与局部搜索、机器学习等算法整合使用，使得多目标算法能够在更广泛的实际问题中得以应用。
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